In your final group assignment you have to analyse data about Airbnb listings and fit a model to predict the total cost for two people staying 4 nights in an AirBnB in a city. You can download AirBnB data from insideairbnb.com; it was originally scraped from airbnb.com.
The following Google sheet shows which cities you can use; please choose one of them and add your group name next to it, e.g., A7, B13. No city can have more than 2 groups per stream working on it; if this happens, I will allocate study groups to cities with the help of R’s sampling.
All of the listings are a GZ file, namely they are archive files compressed by the standard GNU zip (gzip) compression algorithm. You can download, save and extract the file if you wanted, but vroom::vroom() or readr::read_csv() can immediately read and extract this kind of a file. You should prefer vroom() as it is faster, but if vroom() is limited by a firewall, please use read_csv() instead.
vroom will download the *.gz zipped file, unzip, and provide you with the dataframe.
Even though there are many variables in the dataframe, here is a quick description of some of the variables collected, and you can find a data dictionary here
price = cost per night
property_type: type of accommodation (House, Apartment, etc.)
room_type:
number_of_reviews: Total number of reviews for the listing
review_scores_rating: Average review score (0 - 100)
longitude , latitude: geographical coordinates to help us locate the listing
neighbourhood*: three variables on a few major neighbourhoods in each city
In the R4DS Exploratory Data Analysis chapter, the authors state:
“Your goal during EDA is to develop an understanding of your data. The easiest way to do this is to use questions as tools to guide your investigation… EDA is fundamentally a creative process. And like most creative processes, the key to asking quality questions is to generate a large quantity of questions.”
Conduct a thorough EDA. Recall that an EDA involves three things:
dplyr::glimpse()mosaic::favstats()skimr::skim()ggplot2::ggplot()
geom_histogram() or geom_density() for numeric continuous variablesgeom_bar() or geom_col() for categorical variablesGGally::ggpairs() for scaterrlot/correlation matrix
aes call, for example: aes(colour = gender, alpha = 0.4)You may wish to have a level 1 header (#) for your EDA, then use level 2 sub-headers (##) to make sure you cover all three EDA bases. At a minimum you should address these questions:
At this stage, you may also find you want to use filter, mutate, arrange, select, or count. Let your questions lead you!
In all cases, please think about the message your plot is conveying. Don’t just say “This is my X-axis, this is my Y-axis”, but rather what’s the so what of the plot. Tell some sort of story and speculate about the differences in the patterns in no more than a paragraph.
#glimpse function allowed us to see all the variables in the dataset and their types. We noticed that some numeric variables were categorised as character variables, e.g., price
glimpse(listings) Rows: 27,805
Columns: 74
$ id <dbl> 24963, 322045, 402315, 47…
$ listing_url <chr> "https://www.airbnb.com/r…
$ scrape_id <dbl> 2.021093e+13, 2.021093e+1…
$ last_scraped <date> 2021-09-29, 2021-09-28, …
$ name <chr> "Heart of French Built Mu…
$ description <chr> "The flat is located in t…
$ neighborhood_overview <chr> "It's Shanghai Music Conc…
$ picture_url <chr> "https://a0.muscache.com/…
$ host_id <dbl> 98203, 681552, 681552, 68…
$ host_url <chr> "https://www.airbnb.com/u…
$ host_name <chr> "Jia", "Leon", "Leon", "L…
$ host_since <date> 2010-03-24, 2011-06-09, …
$ host_location <chr> "Shanghai, Shanghai, Chin…
$ host_about <chr> "I am an architect, train…
$ host_response_time <chr> "N/A", "within an hour", …
$ host_response_rate <chr> "N/A", "100%", "100%", "1…
$ host_acceptance_rate <chr> "N/A", "100%", "100%", "1…
$ host_is_superhost <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ host_thumbnail_url <chr> "https://a0.muscache.com/…
$ host_picture_url <chr> "https://a0.muscache.com/…
$ host_neighbourhood <chr> "Conservatory", "Changsho…
$ host_listings_count <dbl> 2, 16, 16, 16, 16, 16, 16…
$ host_total_listings_count <dbl> 2, 16, 16, 16, 16, 16, 16…
$ host_verifications <chr> "['email', 'phone', 'revi…
$ host_has_profile_pic <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ host_identity_verified <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ neighbourhood <chr> "Shanghai, China", "Shang…
$ neighbourhood_cleansed <chr> "徐汇区 / Xuhui District"…
$ neighbourhood_group_cleansed <lgl> NA, NA, NA, NA, NA, NA, N…
$ latitude <dbl> 31.21073, 31.24240, 31.24…
$ longitude <dbl> 121.4516, 121.4445, 121.4…
$ property_type <chr> "Entire rental unit", "En…
$ room_type <chr> "Entire home/apt", "Entir…
$ accommodates <dbl> 3, 2, 2, 2, 2, 2, 3, 2, 1…
$ bathrooms <lgl> NA, NA, NA, NA, NA, NA, N…
$ bathrooms_text <chr> "1 bath", "1 bath", "1 ba…
$ bedrooms <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ beds <dbl> 2, 1, 1, 1, 1, 1, 2, 1, 1…
$ amenities <chr> "[\"Smoke alarm\", \"Sham…
$ price <chr> "$480.00", "$464.00", "$4…
$ minimum_nights <dbl> 3, 1, 1, 1, 1, 1, 1, 1, 3…
$ maximum_nights <dbl> 365, 1125, 1125, 1125, 11…
$ minimum_minimum_nights <dbl> 3, 1, 1, 1, 1, 1, 1, 1, 3…
$ maximum_minimum_nights <dbl> 3, 1, 1, 1, 1, 1, 1, 1, 3…
$ minimum_maximum_nights <dbl> 365, 1125, 1125, 1125, 11…
$ maximum_maximum_nights <dbl> 365, 1125, 1125, 1125, 11…
$ minimum_nights_avg_ntm <dbl> 3, 1, 1, 1, 1, 1, 1, 1, 3…
$ maximum_nights_avg_ntm <dbl> 365, 1125, 1125, 1125, 11…
$ calendar_updated <lgl> NA, NA, NA, NA, NA, NA, N…
$ has_availability <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ availability_30 <dbl> 0, 0, 0, 25, 0, 21, 22, 2…
$ availability_60 <dbl> 0, 0, 28, 55, 0, 51, 52, …
$ availability_90 <dbl> 0, 0, 58, 85, 0, 81, 82, …
$ availability_365 <dbl> 240, 242, 333, 360, 41, 3…
$ calendar_last_scraped <date> 2021-09-29, 2021-09-28, …
$ number_of_reviews <dbl> 85, 42, 27, 28, 34, 77, 3…
$ number_of_reviews_ltm <dbl> 0, 0, 7, 0, 0, 10, 14, 10…
$ number_of_reviews_l30d <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0…
$ first_review <date> 2012-10-15, 2014-12-14, …
$ last_review <date> 2019-11-22, 2017-11-13, …
$ review_scores_rating <dbl> 4.74, 4.78, 4.69, 4.36, 4…
$ review_scores_accuracy <dbl> 4.87, 4.64, 4.76, 4.23, 4…
$ review_scores_cleanliness <dbl> 4.54, 4.52, 4.72, 4.12, 4…
$ review_scores_checkin <dbl> 4.77, 4.80, 4.72, 4.50, 4…
$ review_scores_communication <dbl> 4.70, 4.86, 4.96, 4.65, 4…
$ review_scores_location <dbl> 4.86, 4.59, 4.80, 4.58, 4…
$ review_scores_value <dbl> 4.76, 4.56, 4.76, 4.42, 4…
$ license <lgl> NA, NA, NA, NA, NA, NA, N…
$ instant_bookable <lgl> FALSE, TRUE, TRUE, TRUE, …
$ calculated_host_listings_count <dbl> 1, 16, 16, 16, 16, 16, 16…
$ calculated_host_listings_count_entire_homes <dbl> 1, 16, 16, 16, 16, 16, 16…
$ calculated_host_listings_count_private_rooms <dbl> 0, 0, 0, 0, 0, 0, 0, 2, 1…
$ calculated_host_listings_count_shared_rooms <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0…
$ reviews_per_month <dbl> 0.78, 0.51, 0.24, 0.25, 0…
#This function gave us an insignt into the missing values and summary statistics for each variable
skim(listings) | Name | listings |
| Number of rows | 27805 |
| Number of columns | 74 |
| _______________________ | |
| Column type frequency: | |
| character | 23 |
| Date | 5 |
| logical | 9 |
| numeric | 37 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| listing_url | 0 | 1.00 | 34 | 37 | 0 | 27805 | 0 |
| name | 0 | 1.00 | 1 | 113 | 0 | 26678 | 0 |
| description | 1187 | 0.96 | 1 | 1000 | 0 | 22493 | 0 |
| neighborhood_overview | 4778 | 0.83 | 1 | 1000 | 0 | 15123 | 0 |
| picture_url | 0 | 1.00 | 61 | 126 | 0 | 26684 | 0 |
| host_url | 0 | 1.00 | 39 | 43 | 0 | 7561 | 0 |
| host_name | 17 | 1.00 | 1 | 42 | 0 | 5557 | 0 |
| host_location | 33 | 1.00 | 2 | 51 | 0 | 171 | 0 |
| host_about | 11312 | 0.59 | 1 | 3298 | 0 | 3849 | 22 |
| host_response_time | 12 | 1.00 | 3 | 18 | 0 | 5 | 0 |
| host_response_rate | 12 | 1.00 | 2 | 4 | 0 | 53 | 0 |
| host_acceptance_rate | 12 | 1.00 | 2 | 4 | 0 | 82 | 0 |
| host_thumbnail_url | 12 | 1.00 | 55 | 106 | 0 | 7530 | 0 |
| host_picture_url | 12 | 1.00 | 57 | 109 | 0 | 7530 | 0 |
| host_neighbourhood | 13506 | 0.51 | 2 | 31 | 0 | 101 | 0 |
| host_verifications | 0 | 1.00 | 2 | 179 | 0 | 323 | 0 |
| neighbourhood | 4778 | 0.83 | 15 | 35 | 0 | 25 | 0 |
| neighbourhood_cleansed | 0 | 1.00 | 13 | 24 | 0 | 16 | 0 |
| property_type | 0 | 1.00 | 4 | 35 | 0 | 89 | 0 |
| room_type | 0 | 1.00 | 10 | 15 | 0 | 4 | 0 |
| bathrooms_text | 55 | 1.00 | 6 | 17 | 0 | 72 | 0 |
| amenities | 0 | 1.00 | 16 | 1304 | 0 | 21170 | 0 |
| price | 0 | 1.00 | 5 | 10 | 0 | 3289 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| last_scraped | 0 | 1.00 | 2021-09-28 | 2021-10-05 | 2021-09-29 | 4 |
| host_since | 12 | 1.00 | 2010-03-24 | 2021-09-27 | 2018-12-18 | 2474 |
| calendar_last_scraped | 0 | 1.00 | 2021-09-28 | 2021-10-05 | 2021-09-29 | 4 |
| first_review | 10374 | 0.63 | 2012-07-03 | 2021-09-29 | 2020-01-13 | 1933 |
| last_review | 10374 | 0.63 | 2012-07-07 | 2021-09-30 | 2021-03-26 | 1519 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| host_is_superhost | 12 | 1 | 0.36 | FAL: 17690, TRU: 10103 |
| host_has_profile_pic | 12 | 1 | 1.00 | TRU: 27756, FAL: 37 |
| host_identity_verified | 12 | 1 | 1.00 | TRU: 27781, FAL: 12 |
| neighbourhood_group_cleansed | 27805 | 0 | NaN | : |
| bathrooms | 27805 | 0 | NaN | : |
| calendar_updated | 27805 | 0 | NaN | : |
| has_availability | 0 | 1 | 1.00 | TRU: 27802, FAL: 3 |
| license | 27805 | 0 | NaN | : |
| instant_bookable | 0 | 1 | 0.66 | TRU: 18326, FAL: 9479 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 4.030854e+07 | 9889734.37 | 2.496300e+04 | 3.510384e+07 | 4.314768e+07 | 4.825886e+07 | 5.250628e+07 | ▁▁▂▅▇ |
| scrape_id | 0 | 1.00 | 2.021093e+13 | 0.00 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | ▁▁▇▁▁ |
| host_id | 0 | 1.00 | 2.236987e+08 | 115231469.53 | 9.820300e+04 | 1.308527e+08 | 2.312615e+08 | 3.189058e+08 | 4.247836e+08 | ▆▇▇▇▇ |
| host_listings_count | 12 | 1.00 | 2.395000e+01 | 76.00 | 0.000000e+00 | 1.000000e+00 | 6.000000e+00 | 1.600000e+01 | 1.100000e+03 | ▇▁▁▁▁ |
| host_total_listings_count | 12 | 1.00 | 2.395000e+01 | 76.00 | 0.000000e+00 | 1.000000e+00 | 6.000000e+00 | 1.600000e+01 | 1.100000e+03 | ▇▁▁▁▁ |
| latitude | 0 | 1.00 | 3.120000e+01 | 0.14 | 3.071000e+01 | 3.114000e+01 | 3.120000e+01 | 3.123000e+01 | 3.183000e+01 | ▁▅▇▁▁ |
| longitude | 0 | 1.00 | 1.215100e+02 | 0.17 | 1.208600e+02 | 1.214400e+02 | 1.214900e+02 | 1.216600e+02 | 1.219400e+02 | ▁▁▇▆▁ |
| accommodates | 0 | 1.00 | 3.780000e+00 | 3.43 | 0.000000e+00 | 2.000000e+00 | 2.000000e+00 | 4.000000e+00 | 1.600000e+01 | ▇▃▁▁▁ |
| bedrooms | 975 | 0.96 | 1.750000e+00 | 1.85 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 5.000000e+01 | ▇▁▁▁▁ |
| beds | 212 | 0.99 | 2.230000e+00 | 2.71 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 5.000000e+01 | ▇▁▁▁▁ |
| minimum_nights | 0 | 1.00 | 6.740000e+00 | 32.99 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| maximum_nights | 0 | 1.00 | 8.670600e+02 | 436.07 | 1.000000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 1.999900e+04 | ▇▁▁▁▁ |
| minimum_minimum_nights | 0 | 1.00 | 6.630000e+00 | 32.69 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| maximum_minimum_nights | 0 | 1.00 | 6.860000e+00 | 33.40 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| minimum_maximum_nights | 0 | 1.00 | 9.347700e+02 | 392.87 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.999900e+04 | ▇▁▁▁▁ |
| maximum_maximum_nights | 0 | 1.00 | 9.364400e+02 | 391.40 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.999900e+04 | ▇▁▁▁▁ |
| minimum_nights_avg_ntm | 0 | 1.00 | 6.740000e+00 | 32.90 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| maximum_nights_avg_ntm | 0 | 1.00 | 9.361200e+02 | 391.44 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.999900e+04 | ▇▁▁▁▁ |
| availability_30 | 0 | 1.00 | 1.912000e+01 | 10.80 | 0.000000e+00 | 6.000000e+00 | 2.400000e+01 | 2.800000e+01 | 3.000000e+01 | ▅▁▁▅▇ |
| availability_60 | 0 | 1.00 | 4.472000e+01 | 18.09 | 0.000000e+00 | 3.500000e+01 | 5.300000e+01 | 5.800000e+01 | 6.000000e+01 | ▁▁▂▁▇ |
| availability_90 | 0 | 1.00 | 7.099000e+01 | 25.64 | 0.000000e+00 | 6.500000e+01 | 8.300000e+01 | 8.700000e+01 | 9.000000e+01 | ▁▁▁▂▇ |
| availability_365 | 0 | 1.00 | 2.493600e+02 | 126.53 | 0.000000e+00 | 9.200000e+01 | 3.360000e+02 | 3.600000e+02 | 3.650000e+02 | ▂▂▂▁▇ |
| number_of_reviews | 0 | 1.00 | 1.234000e+01 | 29.52 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 1.000000e+01 | 4.580000e+02 | ▇▁▁▁▁ |
| number_of_reviews_ltm | 0 | 1.00 | 3.940000e+00 | 8.80 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.000000e+00 | 1.510000e+02 | ▇▁▁▁▁ |
| number_of_reviews_l30d | 0 | 1.00 | 1.900000e-01 | 0.70 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.500000e+01 | ▇▁▁▁▁ |
| review_scores_rating | 10374 | 0.63 | 4.630000e+00 | 0.92 | 0.000000e+00 | 4.710000e+00 | 4.940000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_accuracy | 10852 | 0.61 | 4.830000e+00 | 0.45 | 1.000000e+00 | 4.860000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_cleanliness | 10852 | 0.61 | 4.780000e+00 | 0.47 | 1.000000e+00 | 4.750000e+00 | 4.960000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_checkin | 10854 | 0.61 | 4.860000e+00 | 0.42 | 1.000000e+00 | 4.900000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_communication | 10852 | 0.61 | 4.880000e+00 | 0.40 | 1.000000e+00 | 4.930000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_location | 10854 | 0.61 | 4.840000e+00 | 0.39 | 1.000000e+00 | 4.830000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_value | 10854 | 0.61 | 4.760000e+00 | 0.50 | 1.000000e+00 | 4.750000e+00 | 4.930000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| calculated_host_listings_count | 0 | 1.00 | 1.644000e+01 | 28.05 | 1.000000e+00 | 3.000000e+00 | 9.000000e+00 | 1.700000e+01 | 2.220000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_entire_homes | 0 | 1.00 | 1.037000e+01 | 21.21 | 0.000000e+00 | 1.000000e+00 | 3.000000e+00 | 1.000000e+01 | 1.680000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_private_rooms | 0 | 1.00 | 5.880000e+00 | 14.04 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 7.000000e+00 | 1.280000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_shared_rooms | 0 | 1.00 | 2.000000e-01 | 1.92 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.000000e+01 | ▇▁▁▁▁ |
| reviews_per_month | 10374 | 0.63 | 9.200000e-01 | 1.12 | 1.000000e-02 | 1.900000e-01 | 5.000000e-01 | 1.180000e+00 | 1.263000e+01 | ▇▁▁▁▁ |
# This function allows to convert character type data into a numeric. We do this for price
listings <- listings %>%
mutate(price = parse_number(price))
# We check if the conversion was successful
typeof(listings$price)[1] "double"
# We noriced that the bathroom variable is mostly text, hence we convert it to a numeric using parse function
listings <- listings %>%
mutate(bathrooms = parse_number(bathrooms_text))
favstats(listings$bathrooms)| min | Q1 | median | Q3 | max | mean | sd | n | missing |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1.5 | 50 | 1.57 | 1.72 | 27429 | 376 |
Once you load the data, it’s always a good idea to use glimpse to see what kind of variables you have and what data type (chr, num, logical, date, etc) they are.
Notice that some of the price data (price) is given as a character string, e.g., “$176.00”
Since price is a quantitative variable, we need to make sure it is stored as numeric data num in the dataframe. To do so, we will first use readr::parse_number() which drops any non-numeric characters before or after the first number
listings <- listings %>%
mutate(price = parse_number(price))
Use typeof(listing$price) to confirm that price is now stored as a number.
Next, we look at the variable property_type. We can use the count function to determine how many categories there are their frequency. What are the top 4 most common property types? What proportion of the total listings do they make up?
property_type_by_proportion <- listings %>%
count(property_type) %>%
arrange(desc(n)) %>%
mutate(proportion = n/sum(n)*100)
property_type_by_proportion| property_type | n | proportion |
|---|---|---|
| Entire rental unit | 6620 | 23.8 |
| Private room in villa | 3261 | 11.7 |
| Entire residential home | 2065 | 7.43 |
| Entire villa | 1923 | 6.92 |
| Private room in rental unit | 1878 | 6.75 |
| Entire condominium (condo) | 1656 | 5.96 |
| Entire loft | 1554 | 5.59 |
| Private room in residential home | 1501 | 5.4 |
| Entire serviced apartment | 1357 | 4.88 |
| Private room in serviced apartment | 843 | 3.03 |
| Private room in condominium (condo) | 595 | 2.14 |
| Private room in kezhan | 476 | 1.71 |
| Room in boutique hotel | 463 | 1.67 |
| Private room in farm stay | 423 | 1.52 |
| Private room in bed and breakfast | 333 | 1.2 |
| Private room in townhouse | 279 | 1 |
| Room in hotel | 274 | 0.985 |
| Shared room in rental unit | 252 | 0.906 |
| Private room in loft | 201 | 0.723 |
| Farm stay | 189 | 0.68 |
| Private room in cottage | 189 | 0.68 |
| Entire townhouse | 170 | 0.611 |
| Shared room in hostel | 149 | 0.536 |
| Entire cottage | 128 | 0.46 |
| Private room in resort | 119 | 0.428 |
| Private room in hostel | 108 | 0.388 |
| Room in aparthotel | 105 | 0.378 |
| Private room in guesthouse | 81 | 0.291 |
| Shared room in condominium (condo) | 77 | 0.277 |
| Entire guest suite | 48 | 0.173 |
| Shared room in residential home | 43 | 0.155 |
| Private room in guest suite | 39 | 0.14 |
| Entire bungalow | 29 | 0.104 |
| Entire guesthouse | 29 | 0.104 |
| Private room in bungalow | 23 | 0.0827 |
| Tiny house | 21 | 0.0755 |
| Shared room in villa | 19 | 0.0683 |
| Shared room in loft | 18 | 0.0647 |
| Tent | 17 | 0.0611 |
| Private room in tiny house | 15 | 0.0539 |
| Shared room in guesthouse | 15 | 0.0539 |
| Entire cabin | 14 | 0.0504 |
| Private room in cabin | 14 | 0.0504 |
| Shared room in townhouse | 14 | 0.0504 |
| Shared room in boutique hotel | 12 | 0.0432 |
| Private room | 11 | 0.0396 |
| Private room in minsu | 11 | 0.0396 |
| Earth house | 9 | 0.0324 |
| Entire chalet | 9 | 0.0324 |
| Entire home/apt | 9 | 0.0324 |
| Shared room in bed and breakfast | 9 | 0.0324 |
| Entire place | 8 | 0.0288 |
| Private room in earth house | 8 | 0.0288 |
| Shared room in serviced apartment | 8 | 0.0288 |
| Entire bed and breakfast | 5 | 0.018 |
| Private room in barn | 5 | 0.018 |
| Private room in castle | 5 | 0.018 |
| Private room in nature lodge | 5 | 0.018 |
| Shared room in guest suite | 5 | 0.018 |
| Shared room in tent | 5 | 0.018 |
| Kezhan | 4 | 0.0144 |
| Minsu | 4 | 0.0144 |
| Private room in casa particular | 4 | 0.0144 |
| Camper/RV | 3 | 0.0108 |
| Castle | 3 | 0.0108 |
| Floor | 3 | 0.0108 |
| Private room in chalet | 3 | 0.0108 |
| Private room in tent | 3 | 0.0108 |
| Shared room in farm stay | 3 | 0.0108 |
| Casa particular | 2 | 0.00719 |
| Religious building | 2 | 0.00719 |
| Shared room in aparthotel | 2 | 0.00719 |
| Shared room in casa particular | 2 | 0.00719 |
| Barn | 1 | 0.0036 |
| Campsite | 1 | 0.0036 |
| Entire hostel | 1 | 0.0036 |
| Nature lodge | 1 | 0.0036 |
| Pension | 1 | 0.0036 |
| Private room in boat | 1 | 0.0036 |
| Private room in camper/rv | 1 | 0.0036 |
| Private room in dome house | 1 | 0.0036 |
| Private room in houseboat | 1 | 0.0036 |
| Private room in ranch | 1 | 0.0036 |
| Riad | 1 | 0.0036 |
| Shared room | 1 | 0.0036 |
| Shared room in barn | 1 | 0.0036 |
| Shared room in bungalow | 1 | 0.0036 |
| Shared room in kezhan | 1 | 0.0036 |
| Treehouse | 1 | 0.0036 |
Since the vast majority of the observations in the data are one of the top four or five property types, we would like to create a simplified version of property_type variable that has 5 categories: the top four categories and Other. Fill in the code below to create prop_type_simplified.
listings <- listings %>%
mutate(prop_type_simplified = case_when(
property_type %in% c("Entire rental unit","Private room in villa", "Entire residential home","Entire villa") ~ property_type,
TRUE ~ "Other"
))
listings %>%
count(property_type, prop_type_simplified) %>%
arrange(desc(n)) | property_type | prop_type_simplified | n |
|---|---|---|
| Entire rental unit | Entire rental unit | 6620 |
| Private room in villa | Private room in villa | 3261 |
| Entire residential home | Entire residential home | 2065 |
| Entire villa | Entire villa | 1923 |
| Private room in rental unit | Other | 1878 |
| Entire condominium (condo) | Other | 1656 |
| Entire loft | Other | 1554 |
| Private room in residential home | Other | 1501 |
| Entire serviced apartment | Other | 1357 |
| Private room in serviced apartment | Other | 843 |
| Private room in condominium (condo) | Other | 595 |
| Private room in kezhan | Other | 476 |
| Room in boutique hotel | Other | 463 |
| Private room in farm stay | Other | 423 |
| Private room in bed and breakfast | Other | 333 |
| Private room in townhouse | Other | 279 |
| Room in hotel | Other | 274 |
| Shared room in rental unit | Other | 252 |
| Private room in loft | Other | 201 |
| Farm stay | Other | 189 |
| Private room in cottage | Other | 189 |
| Entire townhouse | Other | 170 |
| Shared room in hostel | Other | 149 |
| Entire cottage | Other | 128 |
| Private room in resort | Other | 119 |
| Private room in hostel | Other | 108 |
| Room in aparthotel | Other | 105 |
| Private room in guesthouse | Other | 81 |
| Shared room in condominium (condo) | Other | 77 |
| Entire guest suite | Other | 48 |
| Shared room in residential home | Other | 43 |
| Private room in guest suite | Other | 39 |
| Entire bungalow | Other | 29 |
| Entire guesthouse | Other | 29 |
| Private room in bungalow | Other | 23 |
| Tiny house | Other | 21 |
| Shared room in villa | Other | 19 |
| Shared room in loft | Other | 18 |
| Tent | Other | 17 |
| Private room in tiny house | Other | 15 |
| Shared room in guesthouse | Other | 15 |
| Entire cabin | Other | 14 |
| Private room in cabin | Other | 14 |
| Shared room in townhouse | Other | 14 |
| Shared room in boutique hotel | Other | 12 |
| Private room | Other | 11 |
| Private room in minsu | Other | 11 |
| Earth house | Other | 9 |
| Entire chalet | Other | 9 |
| Entire home/apt | Other | 9 |
| Shared room in bed and breakfast | Other | 9 |
| Entire place | Other | 8 |
| Private room in earth house | Other | 8 |
| Shared room in serviced apartment | Other | 8 |
| Entire bed and breakfast | Other | 5 |
| Private room in barn | Other | 5 |
| Private room in castle | Other | 5 |
| Private room in nature lodge | Other | 5 |
| Shared room in guest suite | Other | 5 |
| Shared room in tent | Other | 5 |
| Kezhan | Other | 4 |
| Minsu | Other | 4 |
| Private room in casa particular | Other | 4 |
| Camper/RV | Other | 3 |
| Castle | Other | 3 |
| Floor | Other | 3 |
| Private room in chalet | Other | 3 |
| Private room in tent | Other | 3 |
| Shared room in farm stay | Other | 3 |
| Casa particular | Other | 2 |
| Religious building | Other | 2 |
| Shared room in aparthotel | Other | 2 |
| Shared room in casa particular | Other | 2 |
| Barn | Other | 1 |
| Campsite | Other | 1 |
| Entire hostel | Other | 1 |
| Nature lodge | Other | 1 |
| Pension | Other | 1 |
| Private room in boat | Other | 1 |
| Private room in camper/rv | Other | 1 |
| Private room in dome house | Other | 1 |
| Private room in houseboat | Other | 1 |
| Private room in ranch | Other | 1 |
| Riad | Other | 1 |
| Shared room | Other | 1 |
| Shared room in barn | Other | 1 |
| Shared room in bungalow | Other | 1 |
| Shared room in kezhan | Other | 1 |
| Treehouse | Other | 1 |
#this function allows us to get an insight into max, min, mean, meadian values
favstats(listings$minimum_nights) | min | Q1 | median | Q3 | max | mean | sd | n | missing |
|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 1e+03 | 6.74 | 33 | 27805 | 0 |
#this chunk of code builds a density chart for the values and gives an idea of where the most common value is
listings %>%
ggplot(aes(x=minimum_nights))+
geom_density()+
NULL #this chunk of code allows to break down each minimum night value by frequency
listings %>%
count(minimum_nights) %>%
arrange(desc(n))| minimum_nights | n |
|---|---|
| 1 | 23727 |
| 2 | 980 |
| 30 | 919 |
| 3 | 456 |
| 7 | 322 |
| 90 | 199 |
| 5 | 177 |
| 180 | 148 |
| 15 | 123 |
| 365 | 117 |
| 60 | 102 |
| 14 | 69 |
| 10 | 66 |
| 20 | 55 |
| 28 | 50 |
| 4 | 42 |
| 31 | 40 |
| 100 | 31 |
| 120 | 27 |
| 360 | 20 |
| 6 | 17 |
| 25 | 10 |
| 35 | 9 |
| 33 | 8 |
| 50 | 8 |
| 500 | 7 |
| 300 | 6 |
| 150 | 5 |
| 200 | 5 |
| 8 | 4 |
| 56 | 4 |
| 185 | 4 |
| 12 | 3 |
| 40 | 3 |
| 45 | 3 |
| 210 | 3 |
| 270 | 3 |
| 11 | 2 |
| 13 | 2 |
| 91 | 2 |
| 92 | 2 |
| 183 | 2 |
| 240 | 2 |
| 9 | 1 |
| 16 | 1 |
| 17 | 1 |
| 19 | 1 |
| 21 | 1 |
| 23 | 1 |
| 26 | 1 |
| 32 | 1 |
| 38 | 1 |
| 62 | 1 |
| 70 | 1 |
| 80 | 1 |
| 93 | 1 |
| 101 | 1 |
| 109 | 1 |
| 130 | 1 |
| 152 | 1 |
| 188 | 1 |
| 190 | 1 |
| 555 | 1 |
| 1e+03 | 1 |
# this code filters out all long term listings
short_term_listings <- listings %>%
filter(minimum_nights <=4)Airbnb is most commonly used for travel purposes, i.e., as an alternative to traditional hotels. We only want to include listings in our regression analysis that are intended for travel purposes:
minimum_nights?The most common value is 1 night
The value that stands out is the biggest value in this collumn - 1000. We also notices that some values are 365 and 180 days
minimum_nights?We believe that the reason for the 1000 night value is to prevent AirBnb user from booking the room throuhg the AirBnb system. In order to book a room, the user will have to contact the host directly. This is beneficial to the host because he/she bypasses the AirBnb commission
When it comes to 365 and 180 values, these indicate that the host is looking for a long term renter
Filter the airbnb data so that it only includes observations with minimum_nights <= 4
Visualisations of feature distributions and their relations are key to understanding a data set, and they can open up new lines of exploration. While we do not have time to go into all the wonderful geospatial visualisations one can do with R, you can use the following code to start with a map of your city, and overlay all AirBnB coordinates to get an overview of the spatial distribution of AirBnB rentals. For this visualisation we use the leaflet package, which includes a variety of tools for interactive maps, so you can easily zoom in-out, click on a point to get the actual AirBnB listing for that specific point, etc.
The following code, having downloaded a dataframe listings with all AirbnB listings in Milan, will plot on the map all AirBnBs where minimum_nights is less than equal to four (4). You could learn more about leaflet, by following the relevant Datacamp course on mapping with leaflet
leaflet(data = filter(listings, minimum_nights <= 4)) %>%
addProviderTiles("OpenStreetMap.Mapnik") %>%
addCircleMarkers(lng = ~longitude,
lat = ~latitude,
radius = 1,
fillColor = "blue",
fillOpacity = 0.4,
popup = ~listing_url,
label = ~property_type)For the target variable \(Y\), we will use the cost for two people to stay at an Airbnb location for four (4) nights.
Create a new variable called price_4_nights that uses price, and accomodates to calculate the total cost for two people to stay at the Airbnb property for 4 nights. This is the variable \(Y\) we want to explain.
Use histograms or density plots to examine the distributions of price_4_nights and log(price_4_nights). Which variable should you use for the regression model? Why?
For the regression model we should use log of price_4_nights variable, because it is normally distribution. - spend some time on this
Fit a regression model called model1 with the following explanatory variables: prop_type_simplified, number_of_reviews, and review_scores_rating.
review_scores_rating in terms of price_4_nights.The coefficient for the review_scores_rating suggests that the higher are the ratings, the pricier is the apartment - go back to this
prop_type_simplified in terms of price_4_nights.Coefficients for the property_type_simplified suggests that the property type has a statistically significant effect on the price. In particular, if the property type is Entire Villa or Other, it tends to be more expensive
We want to determine if room_type is a significant predictor of the cost for 4 nights, given everything else in the model. Fit a regression model called model2 that includes all of the explananatory variables in model1 plus room_type.
The model 2 shows that the room type is a significant predictor for the price. More specifically, private and shared rooms are less expensive
# This code builds a dataset that only contains accommodations that can host two people and creates a variable for the 4-nights-stay price. It also creates a variable that is a log10 of the price for 4 nigths
short_term_listings_for_2 <- short_term_listings %>%
filter(accommodates ==2) %>%
mutate(price_4_nights = price*4) %>%
mutate(log_price_4_nights = log(price_4_nights,10))
# This code builds a histogram for the room price for 4 nights per 2 people in Shanghai.
short_term_listings_for_2 %>%
ggplot(aes(x=log_price_4_nights)) +
geom_histogram()+
NULL#This bit of code builds a regression model - model1
model1 <- lm(log_price_4_nights ~ prop_type_simplified + review_scores_rating + number_of_reviews, data= short_term_listings_for_2)
summary(model1)
Call:
lm(formula = log_price_4_nights ~ prop_type_simplified + review_scores_rating +
number_of_reviews, data = short_term_listings_for_2)
Residuals:
Min 1Q Median 3Q Max
-0.90326 -0.14583 -0.00672 0.13893 2.19988
Coefficients:
Estimate Std. Error t value
(Intercept) 3.159e+00 1.577e-02 200.286
prop_type_simplifiedEntire residential home 7.655e-03 1.231e-02 0.622
prop_type_simplifiedEntire villa 6.534e-02 1.780e-02 3.670
prop_type_simplifiedOther -9.489e-02 6.683e-03 -14.200
prop_type_simplifiedPrivate room in villa -8.682e-03 8.711e-03 -0.997
review_scores_rating 1.852e-02 3.235e-03 5.727
number_of_reviews 5.252e-05 7.563e-05 0.695
Pr(>|t|)
(Intercept) < 2e-16 ***
prop_type_simplifiedEntire residential home 0.533980
prop_type_simplifiedEntire villa 0.000244 ***
prop_type_simplifiedOther < 2e-16 ***
prop_type_simplifiedPrivate room in villa 0.318958
review_scores_rating 1.06e-08 ***
number_of_reviews 0.487386
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2376 on 7853 degrees of freedom
(4449 observations deleted due to missingness)
Multiple R-squared: 0.04613, Adjusted R-squared: 0.0454
F-statistic: 63.29 on 6 and 7853 DF, p-value: < 2.2e-16
#This code gives an insight into the room_type variable
unique(short_term_listings_for_2$room_type)[1] "Entire home/apt" "Private room" "Shared room"
#This code builds a model that includes the room type
model2 <- lm(log_price_4_nights ~ prop_type_simplified + review_scores_rating + number_of_reviews + room_type, data= short_term_listings_for_2)
summary(model2)
Call:
lm(formula = log_price_4_nights ~ prop_type_simplified + review_scores_rating +
number_of_reviews + room_type, data = short_term_listings_for_2)
Residuals:
Min 1Q Median 3Q Max
-0.90439 -0.14246 -0.01569 0.12353 2.20387
Coefficients:
Estimate Std. Error t value
(Intercept) 3.163e+00 1.527e-02 207.140
prop_type_simplifiedEntire residential home 8.123e-03 1.190e-02 0.682
prop_type_simplifiedEntire villa 6.428e-02 1.722e-02 3.733
prop_type_simplifiedOther 1.041e-02 8.122e-03 1.282
prop_type_simplifiedPrivate room in villa 1.459e-01 1.133e-02 12.872
review_scores_rating 1.817e-02 3.131e-03 5.804
number_of_reviews -3.433e-05 7.324e-05 -0.469
room_typePrivate room -1.544e-01 7.588e-03 -20.351
room_typeShared room -4.020e-01 2.691e-02 -14.938
Pr(>|t|)
(Intercept) < 2e-16 ***
prop_type_simplifiedEntire residential home 0.494997
prop_type_simplifiedEntire villa 0.000191 ***
prop_type_simplifiedOther 0.200032
prop_type_simplifiedPrivate room in villa < 2e-16 ***
review_scores_rating 6.74e-09 ***
number_of_reviews 0.639307
room_typePrivate room < 2e-16 ***
room_typeShared room < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2298 on 7851 degrees of freedom
(4449 observations deleted due to missingness)
Multiple R-squared: 0.108, Adjusted R-squared: 0.1071
F-statistic: 118.8 on 8 and 7851 DF, p-value: < 2.2e-16
Our dataset has many more variables, so here are some ideas on how you can extend yskimour analysis
bathrooms, bedrooms, beds, or size of the house (accomodates) significant predictors of price_4_nights? Or might these be co-linear variables?# We create a dataset that contains flats with all accommodation capacities and generate a price for 4 nights variable. We also convert bathroom_text into a numeric variable
short_term_listings <- short_term_listings %>%
mutate(price_4_nights = price*4) %>%
mutate(log_price_4_nights = log(price_4_nights,10)) %>%
mutate(bathrooms_text = parse_number(bathrooms_text))
# We create a model that tests the effect of barhrooms, bedrooms and beds
model3 <- lm(log_price_4_nights ~ bathrooms + bedrooms + beds, data= short_term_listings)
summary(model3)
Call:
lm(formula = log_price_4_nights ~ bathrooms + bedrooms + beds,
data = short_term_listings)
Residuals:
Min 1Q Median 3Q Max
-4.5659 -0.1735 -0.0005 0.1767 2.2170
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.101475 0.002922 1061.566 <2e-16 ***
bathrooms 0.022764 0.002286 9.957 <2e-16 ***
bedrooms 0.091740 0.002294 39.995 <2e-16 ***
beds 0.018991 0.001448 13.111 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3266 on 24038 degrees of freedom
(1163 observations deleted due to missingness)
Multiple R-squared: 0.3866, Adjusted R-squared: 0.3866
F-statistic: 5051 on 3 and 24038 DF, p-value: < 2.2e-16
#We check for collinearity using a diagnostics test
vif(model3)bathrooms bedrooms beds
3.888600 4.374824 3.785109
# produce scatterplot-correlation matrix between all explanatory variables
short_term_listings %>%
select(c(bedrooms, bathrooms, beds)) %>%
ggpairs(alpha = 0.3) (host_is_superhost) command a pricing premium, after controlling for other variables?model_Superhost <- lm(log_price_4_nights ~ room_type + review_scores_rating + beds + host_is_superhost, data= short_term_listings)
summary(model_Superhost)
Call:
lm(formula = log_price_4_nights ~ room_type + review_scores_rating +
beds + host_is_superhost, data = short_term_listings)
Residuals:
Min 1Q Median 3Q Max
-4.1921 -0.1670 -0.0209 0.1493 2.1920
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1639188 0.0121336 260.757 < 2e-16 ***
room_typePrivate room -0.1874041 0.0046330 -40.450 < 2e-16 ***
room_typeShared room -0.7208898 0.0142316 -50.654 < 2e-16 ***
review_scores_rating 0.0114577 0.0025435 4.505 6.7e-06 ***
beds 0.0834936 0.0009063 92.126 < 2e-16 ***
host_is_superhostTRUE 0.0446902 0.0045234 9.880 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2789 on 16335 degrees of freedom
(8864 observations deleted due to missingness)
Multiple R-squared: 0.4549, Adjusted R-squared: 0.4548
F-statistic: 2727 on 5 and 16335 DF, p-value: < 2.2e-16
instant_bookable == TRUE), while a non-trivial proportion don’t. After controlling for other variables, is instant_bookable a significant predictor of price_4_nights?# exploring raw materials and summary statistics for instant bookable
skim(short_term_listings$instant_bookable)| Name | short_term_listings$insta… |
| Number of rows | 25205 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| logical | 1 |
| ________________________ | |
| Group variables | None |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| data | 0 | 1 | 0.69 | TRU: 17458, FAL: 7747 |
# regression model fitting
model_instant <- lm(log_price_4_nights ~ room_type+review_scores_rating + beds + host_is_superhost + instant_bookable, data=short_term_listings)
summary(model_instant)
Call:
lm(formula = log_price_4_nights ~ room_type + review_scores_rating +
beds + host_is_superhost + instant_bookable, data = short_term_listings)
Residuals:
Min 1Q Median 3Q Max
-4.1698 -0.1659 -0.0218 0.1482 2.2201
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1439908 0.0123103 255.395 < 2e-16 ***
room_typePrivate room -0.1857387 0.0046258 -40.153 < 2e-16 ***
room_typeShared room -0.7136245 0.0142212 -50.180 < 2e-16 ***
review_scores_rating 0.0092219 0.0025499 3.617 0.000299 ***
beds 0.0835014 0.0009041 92.354 < 2e-16 ***
host_is_superhostTRUE 0.0374580 0.0045853 8.169 3.33e-16 ***
instant_bookableTRUE 0.0450179 0.0050617 8.894 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2782 on 16334 degrees of freedom
(8864 observations deleted due to missingness)
Multiple R-squared: 0.4576, Adjusted R-squared: 0.4574
F-statistic: 2296 on 6 and 16334 DF, p-value: < 2.2e-16
# check for collinearity using a diagnostics test
vif(model_instant) GVIF Df GVIF^(1/(2*Df))
room_type 1.058736 2 1.014371
review_scores_rating 1.075508 1 1.037067
beds 1.042461 1 1.021010
host_is_superhost 1.108619 1 1.052910
instant_bookable 1.058310 1 1.028742
neighbourhood, neighbourhood_cleansed, and neighbourhood_group_cleansed. There are typically more than 20 neighbourhoods in each city, and it wouldn’t make sense to include them all in your model. Use your city knowledge, or ask someone with city knowledge, and see whether you can group neighbourhoods together so the majority of listings falls in fewer (5-6 max) geographical areas. You would thus need to create a new categorical variabale neighbourhood_simplified and determine whether location is a predictor of price_4_nights#For Shanghai, we notice that the data in "neighbourhood" only consists "Shanghai, China" and "NA" while the data in "neighbourhood_group_cleansed" only consists "NA". "Neighbourhood_cleansed" represents different districts in Shanghai. There are altogether 16 districts in Shanghai, so we group different districts based on their distance from the city center and establish a scoring system. Intuitively, we would expect apartments that are in urban areas would have a higher price. For example, Huangpu and Jing'an are the districts nearest to the city center so they score 5.
#Huangpu, Jing'an - tier 1 districts (city center), score 5
#Changning, Xuhui, Yangpu, Hongkou, Putuo - tier 2 districts (urban area), score 4
#Pudong - tier 3 districts (Pudong is a large district, half in urban area, half on the outskirt), score 3
#Baoshan, Jiading, Minhang, Songjiang, Qingpu, Fengxian, Jinshan -tier 4 districts (outskirt of Shanghai), score 2
#Chongming - tier 5 districts (island in Shanghai), score 1
listings_neighbourhood <- short_term_listings %>%
mutate(neighbourhood_simplified =
case_when(neighbourhood_cleansed %in% c("黄浦区 / Huangpu District", "静安区 / Jing'an District")~ 1,
neighbourhood_cleansed %in% c("长宁区 / Changning District", "徐汇区 / Xuhui District", "杨浦区 / Yangpu District", "虹口区 / Hongkou District","普陀区 / Putuo District")~2,
neighbourhood_cleansed %in% c("浦东新区 / Pudong")~3,
neighbourhood_cleansed %in% c("宝山区 / Baoshan District","嘉定区 / Jiading District","闵行区 / Minhang District","松江区 / Songjiang District","青浦区 / Qingpu District","奉贤区 / Fengxian District","金山区 / Jinshan District")~4,
neighbourhood_cleansed %in% c("崇明区 / Chongming District")~5))
#check if we cover all districts
unique(listings_neighbourhood$neighbourhood_simplified) [1] 2 1 3 5 4
# Neighbourhood_simplified is numeric. For the model to run correctly, it needs to be a factor variable.
listings_neighbourhood$neighbourhood_simplified <- as.factor(listings_neighbourhood$neighbourhood_simplified)
# final model
model_neighbourhood <- lm(log_price_4_nights ~ room_type+review_scores_rating + beds + host_is_superhost + instant_bookable+neighbourhood_simplified, data=listings_neighbourhood)
summary(model_neighbourhood)
Call:
lm(formula = log_price_4_nights ~ room_type + review_scores_rating +
beds + host_is_superhost + instant_bookable + neighbourhood_simplified,
data = listings_neighbourhood)
Residuals:
Min 1Q Median 3Q Max
-4.0756 -0.1626 -0.0252 0.1403 2.2534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2050812 0.0128273 249.864 < 2e-16 ***
room_typePrivate room -0.1916452 0.0049393 -38.800 < 2e-16 ***
room_typeShared room -0.7142902 0.0141357 -50.531 < 2e-16 ***
review_scores_rating 0.0075184 0.0025250 2.978 0.00291 **
beds 0.0805626 0.0009561 84.265 < 2e-16 ***
host_is_superhostTRUE 0.0374241 0.0045912 8.151 3.86e-16 ***
instant_bookableTRUE 0.0397366 0.0050346 7.893 3.14e-15 ***
neighbourhood_simplified2 -0.0779772 0.0070067 -11.129 < 2e-16 ***
neighbourhood_simplified3 -0.0295876 0.0064338 -4.599 4.28e-06 ***
neighbourhood_simplified4 -0.1016003 0.0072010 -14.109 < 2e-16 ***
neighbourhood_simplified5 0.0574076 0.0113471 5.059 4.25e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2752 on 16330 degrees of freedom
(8864 observations deleted due to missingness)
Multiple R-squared: 0.4695, Adjusted R-squared: 0.4692
F-statistic: 1445 on 10 and 16330 DF, p-value: < 2.2e-16
avalability_30 or reviews_per_month on price_4_nights, after we control for other variables?# using the data from the previous model to check if availability affects the price
model_availability30 <- lm(log_price_4_nights ~ room_type+review_scores_rating + beds + host_is_superhost + instant_bookable+neighbourhood_simplified+availability_30+number_of_reviews, data=listings_neighbourhood)
summary(model_availability30)
Call:
lm(formula = log_price_4_nights ~ room_type + review_scores_rating +
beds + host_is_superhost + instant_bookable + neighbourhood_simplified +
availability_30 + number_of_reviews, data = listings_neighbourhood)
Residuals:
Min 1Q Median 3Q Max
-4.0252 -0.1622 -0.0266 0.1370 2.2244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.174e+00 1.312e-02 241.985 < 2e-16 ***
room_typePrivate room -1.937e-01 4.919e-03 -39.372 < 2e-16 ***
room_typeShared room -7.245e-01 1.409e-02 -51.426 < 2e-16 ***
review_scores_rating 8.110e-03 2.519e-03 3.220 0.00128 **
beds 8.012e-02 9.525e-04 84.123 < 2e-16 ***
host_is_superhostTRUE 3.962e-02 4.649e-03 8.524 < 2e-16 ***
instant_bookableTRUE 2.685e-02 5.126e-03 5.239 1.64e-07 ***
neighbourhood_simplified2 -8.110e-02 6.979e-03 -11.621 < 2e-16 ***
neighbourhood_simplified3 -3.789e-02 6.446e-03 -5.878 4.24e-09 ***
neighbourhood_simplified4 -1.116e-01 7.249e-03 -15.391 < 2e-16 ***
neighbourhood_simplified5 4.469e-02 1.136e-02 3.932 8.45e-05 ***
availability_30 2.558e-03 2.168e-04 11.801 < 2e-16 ***
number_of_reviews -2.960e-04 6.436e-05 -4.599 4.27e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2738 on 16328 degrees of freedom
(8864 observations deleted due to missingness)
Multiple R-squared: 0.4748, Adjusted R-squared: 0.4744
F-statistic: 1230 on 12 and 16328 DF, p-value: < 2.2e-16
##Our Best Model
huxreg(model3, model_Superhost, model_instant, model_neighbourhood, model_availability30)| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| (Intercept) | 3.101 *** | 3.164 *** | 3.144 *** | 3.205 *** | 3.174 *** |
| (0.003) | (0.012) | (0.012) | (0.013) | (0.013) | |
| bathrooms | 0.023 *** | ||||
| (0.002) | |||||
| bedrooms | 0.092 *** | ||||
| (0.002) | |||||
| beds | 0.019 *** | 0.083 *** | 0.084 *** | 0.081 *** | 0.080 *** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| room_typePrivate room | -0.187 *** | -0.186 *** | -0.192 *** | -0.194 *** | |
| (0.005) | (0.005) | (0.005) | (0.005) | ||
| room_typeShared room | -0.721 *** | -0.714 *** | -0.714 *** | -0.725 *** | |
| (0.014) | (0.014) | (0.014) | (0.014) | ||
| review_scores_rating | 0.011 *** | 0.009 *** | 0.008 ** | 0.008 ** | |
| (0.003) | (0.003) | (0.003) | (0.003) | ||
| host_is_superhostTRUE | 0.045 *** | 0.037 *** | 0.037 *** | 0.040 *** | |
| (0.005) | (0.005) | (0.005) | (0.005) | ||
| instant_bookableTRUE | 0.045 *** | 0.040 *** | 0.027 *** | ||
| (0.005) | (0.005) | (0.005) | |||
| neighbourhood_simplified2 | -0.078 *** | -0.081 *** | |||
| (0.007) | (0.007) | ||||
| neighbourhood_simplified3 | -0.030 *** | -0.038 *** | |||
| (0.006) | (0.006) | ||||
| neighbourhood_simplified4 | -0.102 *** | -0.112 *** | |||
| (0.007) | (0.007) | ||||
| neighbourhood_simplified5 | 0.057 *** | 0.045 *** | |||
| (0.011) | (0.011) | ||||
| availability_30 | 0.003 *** | ||||
| (0.000) | |||||
| number_of_reviews | -0.000 *** | ||||
| (0.000) | |||||
| N | 24042 | 16341 | 16341 | 16341 | 16341 |
| R2 | 0.387 | 0.455 | 0.458 | 0.470 | 0.475 |
| logLik | -7206.826 | -2317.836 | -2278.365 | -2095.955 | -2014.487 |
| AIC | 14423.652 | 4649.672 | 4572.730 | 4215.910 | 4056.974 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | |||||
As you keep building your models, it makes sense to:
Check the residuals, using autoplot(model_x)
As you start building models with more explanatory variables, make sure you use `car::vif(model_x)`` to calculate the Variance Inflation Factor (VIF) for your predictors and determine whether you have colinear variables. A general guideline is that a VIF larger than 5 or 10 is large, and your model may suffer from collinearity. Remove the variable in question and run your model again without it.
Create a summary table, using huxtable (https://mfa2022.netlify.app/example/modelling_side_by_side_tables/) that shows which models you worked on, which predictors are significant, the adjusted \(R^2\), and the Residual Standard Error.
# Create a table that shows the models produced in this analysis
huxreg(model1, model2, model3)| (1) | (2) | (3) | |
|---|---|---|---|
| (Intercept) | 3.159 *** | 3.163 *** | 3.101 *** |
| (0.016) | (0.015) | (0.003) | |
| prop_type_simplifiedEntire residential home | 0.008 | 0.008 | |
| (0.012) | (0.012) | ||
| prop_type_simplifiedEntire villa | 0.065 *** | 0.064 *** | |
| (0.018) | (0.017) | ||
| prop_type_simplifiedOther | -0.095 *** | 0.010 | |
| (0.007) | (0.008) | ||
| prop_type_simplifiedPrivate room in villa | -0.009 | 0.146 *** | |
| (0.009) | (0.011) | ||
| review_scores_rating | 0.019 *** | 0.018 *** | |
| (0.003) | (0.003) | ||
| number_of_reviews | 0.000 | -0.000 | |
| (0.000) | (0.000) | ||
| room_typePrivate room | -0.154 *** | ||
| (0.008) | |||
| room_typeShared room | -0.402 *** | ||
| (0.027) | |||
| bathrooms | 0.023 *** | ||
| (0.002) | |||
| bedrooms | 0.092 *** | ||
| (0.002) | |||
| beds | 0.019 *** | ||
| (0.001) | |||
| N | 7860 | 7860 | 24042 |
| R2 | 0.046 | 0.108 | 0.387 |
| logLik | 146.818 | 410.394 | -7206.826 |
| AIC | -277.635 | -800.789 | 14423.652 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | |||
price_4_nights.# log(price) = 3.22 - 0.19*PrivateRoom - 0.72*SharedRoom + 0.01*ReviewScoresRating + 0.08*Beds + 0.04*Superhost + 0.05*InstantBookable -0.003*NumberOfReviews
# log(price) = 3.22 - 0.19*1 - 0.72*0 + 0.01*90 + 0.08*0 + 0.04*0 + 0.05*0 -0.003*10
#predicted_value <- 3.22 - 0.19*1 - 0.72*0 + 0.01*90 + 0.08*0 + 0.04*0 + 0.05*0 -0.003*10
#exp(predicted_value)
applied_filter <- listings_neighbourhood %>%
filter(room_type=="Private room") %>%
filter(number_of_reviews>=10) %>%
filter(review_scores_rating>=4.5)
dataframe <- predict(model_availability30, applied_filter)
10^dataframe 1 2 3 4 5 6
1275.4949 1346.5120 1266.9092 1152.9268 1232.4632 1290.6690
7 8 9 10 11 12
1527.4583 1328.6223 1055.2231 1266.5385 1112.5901 1023.4277
13 14 15 16 17 18
1470.2526 1491.1061 1361.9551 2244.0738 1254.4833 1241.8753
19 20 21 22 23 24
1252.4811 1427.9473 1059.8279 1239.9096 1033.7987 1188.3356
25 26 27 28 29 30
1379.9411 1509.1593 1001.7281 1199.6359 1278.0716 1239.4750
31 32 33 34 35 36
1265.8990 1341.7781 1344.3542 994.9365 1236.9459 1340.3228
37 38 39 40 41 42
1284.7179 1130.1182 1127.7482 1286.8626 1346.1761 1094.8790
43 44 45 46 47 48
1023.2156 1453.9697 1502.6170 1177.5586 1092.3357 1375.4696
49 50 51 52 53 54
1112.2448 1299.8408 1249.2772 1466.7897 1428.8350 1427.7765
55 56 57 58 59 60
1191.9790 1212.7276 1098.5425 1256.1197 1461.8430 1476.8664
61 62 63 64 65 66
1243.3637 1331.4864 1349.6244 1336.4818 1537.1636 1186.3584
67 68 69 70 71 72
1211.1045 978.9252 1208.8643 1337.1491 1051.4769 12920.5103
73 74 75 76 77 78
1630.2401 1759.7858 1231.1624 1332.9888 1513.5606 1157.6274
79 80 81 82 83 84
1179.3057 1548.1918 11125.0625 1522.7418 1313.2731 1301.8893
85 86 87 88 89 90
1107.7698 2127.1264 1099.3073 1027.4517 1363.5661 1023.5860
91 92 93 94 95 96
1050.7414 1333.3550 1264.7306 1572.8629 1239.3559 1566.9995
97 98 99 100 101 102
1603.3765 1311.3665 2167.9078 1127.2700 1036.7542 1279.8258
103 104 105 106 107 108
1031.6869 1341.0623 1432.1550 1005.8615 1258.8788 1235.0554
109 110 111 112 113 114
1288.6782 1124.4084 1505.2307 1018.3595 1450.5588 1218.4467
115 116 117 118 119 120
1627.0898 1026.0759 1141.1901 1116.5573 1198.7846 968.1056
121 122 123 124 125 126
1353.1393 1206.9533 1410.4160 1201.0511 1118.9454 1242.6788
127 128 129 130 131 132
1327.8769 1755.1130 1329.8676 1316.2917 1354.3586 1011.1816
133 134 135 136 137 138
1140.1171 1461.6834 1435.5574 1141.5118 1306.8927 1337.6307
139 140 141 142 143 144
1237.7062 1111.8217 916.9180 1726.7432 1456.8156 1300.1927
145 146 147 148 149 150
1621.5604 1388.1089 1360.7711 1125.9506 1253.7392 1699.5005
151 152 153 154 155 156
2651.8205 1253.0039 1301.6344 997.2945 1707.4567 933.9124
157 158 159 160 161 162
1043.3878 1122.7841 921.1548 1581.9069 987.0457 1831.6469
163 164 165 166 167 168
1535.7438 1504.4754 1493.6973 1517.1293 1514.4827 1503.7311
169 170 171 172 173 174
986.1030 1423.2617 1180.4772 1475.1424 1617.4883 1273.3609
175 176 177 178 179 180
1371.8235 1240.0473 1019.0206 1724.8423 1185.9706 1204.1430
181 182 183 184 185 186
1422.9055 842.3246 1200.9727 1473.1094 1486.8785 1398.3788
187 188 189 190 191 192
1363.5372 1541.0093 1427.0955 1378.9295 1140.6812 1642.5919
193 194 195 196 197 198
1355.5757 1132.2118 1220.3562 1242.8800 1419.4534 1675.6970
199 200 201 202 203 204
1062.3404 1035.6634 1101.8051 1762.4338 1438.7043 1264.7175
205 206 207 208 209 210
1424.9498 1134.7723 1726.2116 1729.7612 1761.8908 980.9668
211 212 213 214 215 216
963.6088 1455.2854 1154.0909 1092.0684 1002.7776 1443.8856
217 218 219 220 221 222
1145.8671 1751.0990 1320.3731 1109.6041 1118.7360 996.3332
223 224 225 226 227 228
1469.9205 1265.5202 1724.5246 1178.1141 1262.4679 1280.4301
229 230 231 232 233 234
1537.0142 1409.7794 986.7082 1411.4209 1068.5323 3723.2077
235 236 237 238 239 240
1890.8069 1542.5322 1204.0464 1228.2785 1466.7458 1789.3862
241 242 243 244 245 246
1159.5924 1763.5002 1554.3579 1340.4348 2005.0977 1566.7120
247 248 249 250 251 252
2025.9392 1448.8384 1621.3316 1424.3835 938.9047 1112.9613
253 254 255 256 257 258
1876.0874 1485.4075 1357.0175 1365.1889 1307.7706 1256.9355
259 260 261 262 263 264
1351.0393 1119.4578 1419.4499 1277.5615 1301.0583 1648.0856
265 266 267 268 269 270
1225.1458 1542.4337 1195.3900 1270.7173 1297.6049 1263.5369
271 272 273 274 275 276
1083.4176 1128.7930 1254.1495 1168.1807 1159.5492 1324.6103
277 278 279 280 281 282
804.4566 1106.7701 1254.9654 1702.8972 5445.0019 1802.1388
283 284 285 286 287 288
1343.1007 1397.5969 1479.2432 1412.6356 1335.9093 1366.1313
289 290 291 292 293 294
1711.9649 1530.3522 1345.0192 1271.1836 1305.4438 1292.7944
295 296 297 298 299 300
1245.0310 1051.8707 1220.2147 1439.1521 1824.9451 2531.9214
301 302 303 304 305 306
1305.4089 1467.8522 3343.3587 1504.6610 1196.1845 1524.3826
307 308 309 310 311 312
1463.4812 1369.4691 1178.3603 1485.3624 1016.3975 1039.1992
313 314 315 316 317 318
15301.4290 1123.7513 1261.8228 1085.6546 1203.9921 1650.0322
319 320 321 322 323 324
1361.8384 1231.9249 3220.1038 2260.1221 1221.8727 1350.2201
325 326 327 328 329 330
1212.5969 1362.7699 1497.7242 2566.0564 1010.4192 1267.6275
331 332 333 334 335 336
1339.6089 1350.4951 1474.6646 1360.2170 1589.8285 1417.1322
337 338 339 340 341 342
1488.0610 1394.9228 1100.7274 1402.4764 1389.2444 1504.1574
343 344 345 346 347 348
1160.6850 1244.2816 1416.2450 1453.8508 1332.3660 1391.0245
349 350 351 352 353 354
1204.8816 1408.2456 1347.3629 1420.0998 1528.5363 1514.2907
355 356 357 358 359 360
1764.5378 1503.2516 1539.8646 1514.9582 1322.4862 1242.4295
361 362 363 364 365 366
1573.1378 1444.4310 1470.2661 1724.7461 1718.4571 1424.4524
367 368 369 370 371 372
1386.2105 1715.8072 2134.5435 1781.3374 1414.8517 1771.1076
373 374 375 376 377 378
1351.5205 1601.5654 1398.2240 1395.8638 995.9153 1212.3719
379 380 381 382 383 384
1067.4168 1557.5291 1508.1403 1444.0060 1234.0110 1765.0111
385 386 387 388 389 390
1434.9143 1368.9258 1249.2790 2162.4112 1789.9908 1428.9157
391 392 393 394 395 396
1425.8876 1475.5552 983.9554 972.9826 1421.4587 1473.8466
397 398 399 400 401 402
1723.2758 1470.4786 1794.4579 1471.6600 1737.7074 2502.2987
403 404 405 406 407 408
2062.5432 1396.3267 1018.9615 1234.6407 1483.9192 1485.3054
409 410 411 412 413 414
1015.7520 1323.5692 1359.1332 1367.0528 1378.5704 1411.1693
415 416 417 418 419 420
1679.5979 1394.0416 1364.3839 1776.1589 1477.3057 1783.6675
421 422 423 424 425 426
1756.2369 2135.8594 2109.3404 1768.2490 1465.9316 1478.6720
427 428 429 430 431 432
1037.3180 1407.5245 1026.1909 1192.0383 1422.8040 1209.8820
433 434 435 436 437 438
1022.5084 1328.2744 1247.6544 1571.5007 1707.5776 1627.4890
439 440 441 442 443 444
2020.7374 1394.0756 1253.4514 1498.8976 1593.0829 1466.6855
445 446 447 448 449 450
1178.4546 1421.2113 1327.2941 1424.1927 1489.2794 1628.3921
451 452 453 454 455 456
1620.2386 1383.3159 1372.5495 1363.2229 1572.7319 1372.0105
457 458 459 460 461 462
1238.7970 1898.5875 1793.1773 1340.0803 1062.5217 1199.3780
463 464 465 466 467 468
1714.5444 1283.2466 1457.0787 1749.0238 1375.3591 1555.6802
469 470 471 472 473 474
1329.0034 1035.5180 1103.9225 1281.7629 1255.5688 1593.3057
475 476 477 478 479 480
1279.2281 1368.7736 1366.8248 1862.6642 1351.7822 1303.4438
481 482 483 484 485 486
1337.6146 1556.5916 1856.2461 1332.5557 1666.6418 1282.3706
487 488 489 490 491 492
2055.6399 1508.6213 985.7487 1532.8121 1218.1094 1124.3992
493 494 495 496 497 498
1308.4000 1222.1741 1583.5230 1168.4008 1537.7668 1824.7368
499 500 501 502 503 504
1266.2248 1335.9646 1183.2345 1230.5743 1228.0451 1384.7836
505 506 507 508 509 510
1490.3057 1437.3756 1258.8662 1369.3883 1435.9294 1338.5226
511 512 513 514 515 516
1262.3857 1202.4203 1427.1163 1470.6493 1268.1608 1030.5578
517 518 519 520 521 522
1344.2850 1317.6361 1387.7570 1398.2015 1042.6687 1249.2707
523 524 525 526 527 528
1578.7252 1701.4041 1743.7700 1729.0309 1744.8602 1135.2259
529 530 531 532 533 534
1222.4602 1475.7510 1167.3115 1823.1396 1122.7135 1271.6489
535 536 537 538 539 540
1142.9393 1176.0804 1498.2922 1505.1633 1224.0730 1087.4987
541 542 543 544 545 546
1379.6650 1186.4852 1803.8153 1732.8998 1229.0848 1731.8414
547 548 549 550 551 552
1458.2430 1589.8250 1811.0559 1403.5066 1702.0547 1429.4905
553 554 555 556 557 558
1421.8335 1472.4847 1915.6039 1343.9482 1252.5336 1537.4070
559 560 561 562 563 564
1390.9889 1878.5414 1862.1947 1504.4893 1568.5332 1420.8944
565 566 567 568 569 570
1558.1114 1397.3755 1540.9217 1811.3260 1501.2088 1399.6496
571 572 573 574 575 576
1368.1495 1434.8707 1487.7277 1165.6239 1261.7074 1217.0343
577 578 579 580 581 582
1840.9782 1424.0714 1252.3950 1432.9991 1145.3051 1109.4439
583 584 585 586 587 588
1220.5532 1220.5532 1471.7707 1213.9950 2104.4523 1790.7922
589 590 591 592 593 594
1336.7768 1495.2453 1258.8662 1336.4129 1378.1308 1382.5840
595 596 597 598 599 600
1420.1221 1526.7390 1524.6475 1307.5424 1790.0877 1449.9160
601 602 603 604 605 606
1484.0082 1457.8364 1449.9974 1314.3703 1736.4152 1302.8501
607 608 609 610 611 612
1683.5857 1149.7145 1446.9086 1030.7723 1342.0979 2269.2781
613 614 615 616 617 618
1149.1317 1744.2257 1860.1668 1560.6119 1734.3354 1500.0480
619 620 621 622 623 624
1352.4121 1354.5100 1214.7871 1190.8506 1641.2684 1676.1796
625 626 627 628 629 630
1532.7578 3254.7600 1315.5215 1378.0472 1094.2382 1234.9406
631 632 633 634 635 636
1504.8437 1451.5345 998.7203 1734.9027 1311.2715 1216.6685
637 638 639 640 641 642
1540.2457 1169.5597 1529.0186 1909.7994 1486.1240 1564.6045
643 644 645 646 647 648
1328.5134 1420.5038 1678.9861 1632.6409 1343.7737 1245.0989
649 650 651 652 653 654
1133.3196 1801.3412 1503.8044 1162.5009 1388.6785 2260.5833
655 656 657 658 659 660
1149.8723 1537.7668 1631.9990 1139.2504 1498.7018 1330.4515
661 662 663 664 665 666
1475.6780 1474.9371 1793.7879 1456.6761 1466.7351 1465.3798
667 668 669 670 671 672
1388.9850 1164.7698 1783.2371 1404.3794 1472.3777 1456.9325
673 674 675 676 677 678
1125.9331 1434.5526 34629.0195 1252.4409 1120.1370 1519.3863
679 680 681 682 683 684
1238.4959 1453.7875 1464.0122 1420.1092 1474.6725 1681.5907
685 686 687 688 689 690
1429.3273 1502.2383 1522.8634 2192.2636 1886.1516 1526.6534
691 692 693 694 695 696
1283.3027 1448.5217 1883.5820 1298.8296 1220.8372 2224.6804
697 698 699 700 701 702
1117.8256 1109.0194 2614.9508 1694.0468 1125.7860 1344.2552
703 704 705 706 707 708
1324.2907 1284.2402 1286.9556 1367.3318 1468.1430 1454.8057
709 710 711 712 713 714
1373.7551 1696.2923 1415.2187 1684.3354 1207.2888 1203.1235
715 716 717 718 719 720
1763.9433 2092.6129 1470.6594 1217.3426 1518.8242 1508.8949
721 722 723 724 725 726
1789.5650 1671.0228 1143.9673 1813.3726 1442.7323 1435.8955
727 728 729 730 731 732
1556.6687 1767.4189 1878.4534 1553.8689 1377.6695 1649.5107
733 734 735 736 737 738
1423.7902 2071.8204 1664.5966 1144.6381 1502.0881 1190.4800
739 740 741 742 743 744
1279.0915 1857.2786 1341.1255 1819.3293 1544.5697 1529.6173
745 746 747 748 749 750
1904.9551 1571.5122 1517.0575 1557.7355 1574.7291 1586.5808
751 752 753 754 755 756
1887.3384 1313.6681 1397.8450 1692.6778 1517.3164 1489.9113
757 758 759 760 761 762
1491.7489 1485.4155 1447.0847 1457.0775 1548.4892 1918.4678
763 764 765 766 767 768
1329.4452 1460.3171 1426.2962 1438.7051 1211.8386 1444.5850
769 770 771 772 773 774
1733.6916 1473.0398 1174.9674 1713.1405 2140.0153 1776.1589
775 776 777 778 779 780
1477.6673 1470.3317 1453.3106 1587.6187 1469.2610 1262.5399
781 782 783 784 785 786
1021.1803 957.4248 1443.5933 1310.3044 1375.6129 1495.9993
787 788 789 790 791 792
1289.8287 1251.5875 1798.1505 1529.9918 1770.1796 1169.7502
793 794 795 796 797 798
1166.2898 1203.2367 1206.2678 1423.4810 1458.7037 1466.6988
799 800 801 802 803 804
15462.0911 1790.2658 1846.3755 1520.8991 1467.0040 1500.3139
805 806 807 808 809 810
1527.5916 1868.0849 1564.9760 1389.3683 1357.7972 1398.5268
811 812 813 814 815 816
2676.9227 1429.6081 1442.4184 1451.1316 1567.3157 1561.2979
817 818 819 820 821 822
1521.9362 1518.6428 1528.1593 1328.3069 1510.2139 1501.7477
823 824 825 826 827 828
1544.0825 1592.8748 1545.3200 1907.6608 1913.2441 1530.0000
829 830 831 832 833 834
1586.8478 1919.7760 1913.2441 1493.1401 1256.0834 1421.9454
835 836 837 838 839 840
1260.4852 1330.9589 1373.9969 1174.0677 1564.1957 1513.1024
841 842 843 844 845 846
1839.5795 2233.6542 1443.3035 1437.6601 1513.1821 1262.7059
847 848 849 850 851 852
1513.1821 1503.0852 1759.2160 1507.2875 1462.5745 1798.4539
853 854 855 856 857 858
1489.9221 1785.1457 1430.4833 1484.3954 1349.1845 1385.1461
859 860 861 862 863 864
1347.2937 1349.0435 1377.7732 1384.7997 1343.7104 1345.3703
865 866 867 868 869 870
1167.4483 1427.2751 1538.9708 1509.3357 1473.1471 1137.6984
871 872 873 874 875 876
1010.8701 1492.0715 1167.6663 1237.4724 1256.3496 1555.6079
877 878 879 880 881 882
1529.0186 1840.0503 1293.9598 1577.6494 1448.0637 1836.3725
883 884 885 886 887 888
1616.1326 1537.3905 1281.3706 1191.6386 1753.8059 1167.7967
889 890 891 892 893 894
1386.2431 1297.4318 1163.9108 1297.8718 1424.5524 1055.5405
895 896 897 898 899 900
1405.7939 1442.5021 1441.6266 1415.4754 1415.5886 1396.0719
901 902 903 904 905 906
1413.2169 1428.9712 1251.0365 1541.9653 1564.2071 1369.3847
907 908 909 910 911 912
1468.9030 1289.0663 946.0260 1129.5133 1334.1445 1132.5889
913 914 915 916 917 918
1369.0012 1625.4935 1615.7600 1649.8617 1619.3580 1373.9341
919 920 921 922 923 924
1362.6473 1424.9038 1691.8462 1941.2849 1596.3440 1918.4678
925 926 927 928 929 930
1584.0200 1251.6613 1459.6837 1122.6928 1309.8887 1583.4078
931 932 933 934 935 936
1396.7544 1644.5433 1661.6549 1239.5162 1358.3561 1433.6492
937 938 939 940 941 942
1687.6558 1354.2527 1715.8213 1423.2139 1521.0148 1583.3397
943 944 945 946 947 948
1529.4205 1241.9069 1249.0192 1577.9526 1575.8912 1290.0692
949 950 951 952 953 954
1072.4912 5057.6743 1340.9503 1425.2811 1780.4472 1405.8118
955 956 957 958 959 960
1468.9686 1500.5912 1717.3037 1702.2953 1441.4460 1730.9194
961 962 963 964 965 966
1577.0513 1251.7279 1480.4741 1450.2364 1405.3370 1270.8844
967 968 969 970 971 972
1258.1681 1531.8624 1270.2276 1414.2721 1219.7567 1645.3270
973 974 975 976 977 978
1758.5942 1274.1760 1270.5498 1102.6408 1635.3829 1862.3257
979 980 981 982 983 984
1239.6478 1636.1350 1500.0937 1470.1238 1337.4641 1813.8820
985 986 987 988 989 990
1763.2521 1884.8664 1569.4539 1558.3994 1533.4990 1534.8088
991 992 993 994 995 996
1491.1919 1855.1901 1820.8903 1530.7409 1505.9028 1848.4255
997 998 999 1000 1001 1002
2251.9080 2225.9638 1525.2397 1824.2902 1507.2794 1516.0955
1003 1004 1005 1006 1007 1008
2566.0558 1519.1849 1350.3442 1265.6899 1255.1795 1208.2704
1009 1010 1011 1012 1013 1014
1297.2230 1225.6862 1060.0070 2021.2899 1443.9955 1561.1137
1015 1016 1017 1018 1019 1020
1533.6905 1579.5006 1841.1012 1818.9728 1842.2264 2174.5585
1021 1022 1023 1024 1025 1026
1849.1188 1461.1989 1870.3150 1908.0347 1395.1114 1595.9381
1027 1028 1029 1030 1031 1032
1053.8400 1314.0438 1158.7674 1518.1719 1566.3555 1541.9653
1033 1034 1035 1036 1037 1038
1493.0692 1478.1386 1850.1387 1197.7896 1532.9099 1148.0207
1039 1040 1041 1042 1043 1044
1356.3169 1455.9426 1785.8970 1507.6397 1520.0362 2152.6489
1045 1046 1047 1048 1049 1050
2093.5908 1798.1049 1437.6637 1357.0407 1741.8291 1454.9505
1051 1052 1053 1054 1055 1056
1762.1467 1735.1445 1435.4244 1455.8503 1905.4177 960.4524
1057 1058 1059 1060 1061 1062
1761.8083 1580.8612 1292.8065 1451.8020 1569.7554 1570.6320
1063 1064 1065 1066 1067 1068
1509.0649 1520.3446 1914.5487 1585.4850 1548.0204 1519.5071
1069 1070 1071 1072 1073 1074
1748.4319 1751.6804 1030.2141 1518.2378 1464.6148 1454.2200
1075 1076 1077 1078 1079 1080
1472.4524 1450.1523 1501.9439 1486.8597 1519.1989 1482.9001
1081 1082 1083 1084 1085 1086
1488.2320 1413.1674 1439.2495 1118.8827 1328.1401 1180.0650
1087 1088 1089 1090 1091 1092
1389.7193 1642.9412 1351.7393 1676.8992 1347.6506 1657.6727
1093 1094 1095 1096 1097 1098
1681.9803 1337.3339 1347.3990 1649.0644 1689.2200 1394.7481
1099 1100 1101 1102 1103 1104
1764.5344 1478.0017 1474.2260 1377.6551 1470.4758 1371.1506
1105 1106 1107 1108 1109 1110
1405.9137 1532.5347 1888.2625 1463.2029 1559.1739 1859.8000
1111 1112 1113 1114 1115 1116
1866.6026 1267.1706 1527.6943 1559.1906 1810.1015 1529.4616
1117 1118 1119 1120 1121 1122
1816.5150 1603.4744 1242.4580 1194.6010 1204.1177 1402.1003
1123 1124 1125 1126 1127 1128
1429.8508 1439.9392 1478.0928 1341.1914 1728.7392 1411.4494
1129 1130 1131 1132 1133 1134
1707.9862 1421.0207 1705.7550 1405.8592 1416.0961 1416.0961
1135 1136 1137 1138 1139 1140
1421.6340 1962.2685 1715.2386 1299.0474 1376.7029 1772.9064
1141 1142 1143 1144 1145 1146
1389.2233 1369.6180 1394.4794 1702.6771 1354.4483 1853.1290
1147 1148 1149 1150 1151 1152
1736.3589 2226.5283 2304.0215 1379.0032 1660.4425 1383.6181
1153 1154 1155 1156 1157 1158
1605.2800 1377.8963 1524.1612 2209.1636 1378.6975 1238.1567
1159 1160 1161 1162 1163 1164
1432.5975 1733.1295 1766.2051 1465.6456 1413.7236 1519.5597
1165 1166 1167 1168 1169 1170
1499.2426 1396.2566 1709.7443 1692.6746 1487.7213 1830.3988
1171 1172 1173 1174 1175 1176
1488.4242 1177.8093 1530.8215 1127.6248 1274.5039 1900.2471
1177 1178 1179 1180 1181 1182
1823.9398 1507.2794 1575.9852 1576.5744 1348.4058 1322.2061
1183 1184 1185 1186 1187 1188
1523.9076 1313.5673 1447.0019 1548.2031 1678.4657 1530.8215
1189 1190 1191 1192 1193 1194
1217.1172 1256.3136 1625.2970 1716.9158 1717.1740 1296.4910
1195 1196 1197 1198 1199 1200
1576.5744 1510.8333 1552.2386 1498.0800 1883.5820 1546.2816
1201 1202 1203 1204 1205 1206
1889.5500 1392.1313 1410.5241 1361.3665 1056.2681 1228.6544
1207 1208 1209 1210 1211 1212
966.9584 1383.9707 1439.6025 1429.0514 1278.3109 1666.3168
1213 1214 1215 1216 1217 1218
1380.3089 1372.8721 1657.6727 1403.5791 1366.5896 1407.9446
1219 1220 1221 1222 1223 1224
1406.5414 1383.0812 1401.7660 1524.7446 1520.7946 1069.7347
1225 1226 1227 1228 1229 1230
1332.6973 1337.6567 1353.7773 1368.7563 1808.3107 1244.3157
1231 1232 1233 1234 1235 1236
1552.0384 1845.1011 1526.2768 1513.9230 1686.3678 1681.9803
1237 1238 1239 1240 1241 1242
1403.6735 1679.7986 1559.3715 1370.9897 1344.7729 1387.5797
1243 1244 1245 1246 1247 1248
1686.9187 1406.5468 1683.8815 1555.2272 1808.9827 1511.3070
1249 1250 1251 1252 1253 1254
1862.7930 1507.6675 1507.1862 1567.3127 1862.3200 1558.1114
1255 1256 1257 1258 1259 1260
1552.8011 1538.9708 1545.3758 1507.9131 1538.7201 1183.0257
1261 1262 1263 1264 1265 1266
1846.3557 1816.5150 1256.9849 1818.9735 1566.2820 1562.7881
1267 1268 1269 1270 1271 1272
1298.7784 1022.3997 1914.5980 1337.7513 1329.9891 1486.3907
1273 1274 1275 1276 1277 1278
1536.6156 1848.3950 1541.2859 1537.8584 1535.4683 1535.2960
1279 1280 1281 1282 1283 1284
1139.3034 1154.9651 1510.6359 1195.4721 1200.8573 1222.5116
1285 1286 1287 1288 1289 1290
1277.1802 1496.4812 1524.1009 1572.8514 1529.7784 1557.2184
1291 1292 1293 1294 1295 1296
1503.1776 1547.2177 1542.6995 1197.8327 1208.7965 1291.9160
1297 1298 1299 1300 1301 1302
1279.6317 1177.1818 1466.7826 1186.4464 1192.8792 1432.0045
1303 1304 1305 1306 1307 1308
1571.5122 2252.0585 1777.4566 1531.2015 1896.8295 1921.0850
1309 1310 1311 1312 1313 1314
1020.3353 1131.2261 1573.2479 1592.6463 1528.8159 1874.2277
1315 1316 1317 1318 1319 1320
5997.9837 1510.3730 1543.1229 1509.6065 1734.0134 1366.0061
1321 1322 1323 1324 1325 1326
1198.8558 1510.8333 1512.8943 1810.7582 1481.9864 1508.7750
1327 1328 1329 1330 1331 1332
1498.4000 1597.5784 1617.7021 1585.5410 1685.0302 1346.1890
1333 1334 1335 1336 1337 1338
1231.6133 1482.1580 1499.0715 1598.8427 1432.0612 1355.1365
1339 1340 1341 1342 1343 1344
1107.1538 1650.0396 1335.0542 1393.3381 1286.0786 1508.9906
1345 1346 1347 1348 1349 1350
1234.6407 1823.6006 1582.3229 1833.2677 1821.7188 1516.8594
1351 1352 1353 1354 1355 1356
1509.1738 1522.4327 961.5202 1470.9726 1398.6106 1581.1826
1357 1358 1359 1360 1361 1362
990.0803 1390.3268 1321.4490 1832.2036 2114.9799 1832.2001
1363 1364 1365 1366 1367 1368
1724.8486 1359.1980 1326.3021 1278.5284 1501.0156 1777.8242
1369 1370 1371 1372 1373 1374
1506.5059 1468.8743 1516.2825 1385.2368 1405.2843 1342.2715
1375 1376 1377 1378 1379 1380
1439.8236 1069.2379 1438.3718 1336.4146 1508.3043 1394.0240
1381 1382 1383 1384 1385 1386
1497.4147 1515.3203 1530.8215 1501.5030 1511.4795 5486.9771
1387 1388 1389 1390 1391 1392
1463.2067 1497.7922 1539.1110 1515.2493 1558.7923 3155.1099
1393 1394 1395 1396 1397 1398
1297.3263 1037.7834 1585.3841 2116.3529 1545.7969 1670.1993
1399 1400 1401 1402 1403 1404
1839.7237 1518.7227 1804.4794 1507.3779 1496.3863 1440.9083
1405 1406 1407 1408 1409 1410
1303.8025 1134.4524 1053.1778 1671.9034 1444.6412 1455.6942
1411 1412 1413 1414 1415 1416
1389.8175 1027.1090 1558.4048 1558.7102 1236.2597 1326.0015
1417 1418 1419 1420 1421 1422
1057.0398 1683.2418 1745.5180 1261.4734 1258.4312 1732.9511
1423 1424 1425 1426 1427 1428
1515.2741 1375.2731 2136.3092 1471.4491 1779.0589 1507.2516
1429 1430 1431 1432 1433 1434
1800.5378 2093.1846 1497.1293 1516.2767 1235.2607 1231.0579
1435 1436 1437 1438 1439 1440
1381.1005 1359.5111 1842.3467 1491.1162 1518.3510 1698.5159
1441 1442 1443 1444 1445 1446
1874.2474 1586.5115 1558.7749 1178.9345 1187.6168 1628.9976
1447 1448 1449 1450 1451 1452
1421.8843 1553.9418 1478.3181 1776.6257 1443.6315 1056.5168
1453 1454 1455 1456 1457 1458
1844.2952 1533.5797 1466.4562 1158.0888 1323.2123 1569.4539
1459 1460 1461 1462 1463 1464
1548.5821 1565.7507 1546.4724 1800.9800 1866.1494 1545.4186
1465 1466 1467 1468 1469 1470
1900.9218 1186.2203 1549.9357 1594.4522 1895.0031 1843.6030
1471 1472 1473 1474 1475 1476
1395.5906 1506.7247 1170.5849 1530.4469 1517.9514 1527.2923
1477 1478 1479 1480 1481 1482
1822.7620 1527.5916 1530.4045 1673.5126 1392.4146 1532.9929
1483 1484 1485 1486 1487 1488
1537.7668 1512.0272 1506.0556 1498.7018 1832.0579 1817.8192
1489 1490 1491 1492 1493 1494
1519.7413 2013.9519 1663.0480 1382.5167 1390.0112 1665.0026
1495 1496 1497 1498 1499 1500
1663.6846 1388.4277 1383.5525 1382.4009 1681.5168 1820.7811
1501 1502 1503 1504 1505 1506
1269.4332 1523.8707 1529.8695 1821.9001 1817.7200 1529.9553
1507 1508 1509 1510 1511 1512
1515.6254 1537.9476 1516.5709 1531.1157 1574.4266 1495.2012
1513 1514 1515 1516 1517 1518
1551.7520 1490.6706 1428.4286 1433.5883 1441.5003 1518.1638
1519 1520 1521 1522 1523 1524
1828.3393 1589.9177 1687.0709 1654.8381 1543.8755 1562.0707
1525 1526 1527 1528 1529 1530
1880.3470 1481.6256 1554.3581 1416.3922 1580.6809 1293.3568
1531 1532 1533 1534 1535 1536
1538.0481 1527.7036 1896.0010 1890.8385 1566.9322 1513.4489
1537 1538 1539 1540 1541 1542
1308.0549 1586.4117 1588.9313 1779.7412 1863.1373 1964.9544
1543 1544 1545 1546 1547 1548
1301.9230 1898.5875 1562.9502 1227.0449 1536.9799 1487.5585
1549 1550 1551 1552 1553 1554
1422.2268 1319.6748 1713.3669 1572.9095 1407.8396 1145.3447
1555 1556 1557 1558 1559 1560
1860.2553 1846.2386 1530.0723 1554.9285 1399.4009 2117.8847
1561 1562 1563 1564 1565 1566
1228.4288 1197.7789 1381.5399 1235.5802 1346.0526 1283.6817
1567 1568 1569 1570 1571 1572
1505.5013 1248.1382 1279.1083 1298.4876 1445.8159 2143.5169
1573 1574 1575 1576 1577 1578
2252.9559 1546.2816 110093.2790 1656.9366 1484.0652 1908.6051
1579 1580 1581 1582 1583 1584
1415.1967 1719.1166 1417.4218 1203.9762 1500.4989 1490.3893
1585 1586 1587 1588 1589 1590
1815.9553 1514.9716 1503.4722 2194.7285 1751.2612 1303.0814
1591 1592 1593 1594 1595 1596
1301.0022 1613.4846 1366.3902 1421.3464 1523.8274 1490.6706
1597 1598 1599 1600 1601 1602
1404.3565 1860.2553 1821.9204 1486.8894 1815.1621 1462.2166
1603 1604 1605 1606 1607 1608
1845.1011 1291.5998 3126.3055 1258.4312 1132.6213 1549.8116
1609 1610 1611 1612 1613 1614
1359.4357 1867.0659 1537.3905 1765.0490 1523.1618 1819.4188
1615 1616 1617 1618 1619 1620
1533.9522 1553.8689 1556.5556 1544.7436 1530.0582 1531.9595
1621 1622 1623 1624 1625 1626
1813.3301 1528.7360 1556.8977 1873.6503 1514.2080 2179.8004
1627 1628 1629 1630 1631 1632
1532.9092 1559.8377 1226.3874 1863.1509 1552.4128 1848.3950
1633 1634 1635 1636 1637 1638
1557.2298 1517.2085 1890.5878 2234.0765 1293.2024 1562.9850
1639 1640 1641 1642 1643 1644
1408.2892 1205.6761 1511.8635 1506.7195 1171.1649 1490.5572
1645 1646 1647 1648 1649 1650
1802.5800 1338.6993 1353.9264 1532.9099 1333.0904 1280.5486
1651 1652 1653 1654 1655 1656
1368.5251 1619.5845 1315.1400 1371.6530 1649.8873 1647.9441
1657 1658 1659 1660 1661 1662
1356.7774 1359.7072 1196.9034 1627.2898 2382.6137 2848.1191
1663 1664 1665 1666 1667 1668
1494.5292 1449.0093 2656.2372 1487.6929 1478.2079 1384.8923
1669 1670 1671 1672 1673 1674
1809.1530 1462.3520 1581.7349 1532.9099 1541.9653 1239.3111
1675 1676 1677 1678 1679 1680
1553.3842 1364.9481 1753.3484 1395.7343 1311.0796 1478.9590
1681 1682 1683 1684 1685 1686
1519.0034 1566.2478 1543.6910 1536.2425 1526.6534 1837.2174
1687 1688 1689 1690 1691 1692
1523.9076 1864.0632 1537.5659 1539.4878 1857.7210 1544.3655
1693 1694 1695 1696 1697 1698
1534.6254 1303.6946 1436.2218 1901.3180 1571.5908 1858.9029
1699 1700 1701 1702 1703 1704
2286.5402 1576.6618 1694.2283 1287.7678 1247.5499 1355.5489
1705 1706 1707 1708 1709 1710
1165.9600 1436.6712 1144.9192 1801.3864 1293.7504 1396.2817
1711 1712 1713 1714 1715 1716
1441.3934 1733.9435 1773.1926 1422.6783 1736.8601 1742.7958
1717 1718 1719 1720 1721 1722
1739.5443 1403.9241 1437.2942 1411.6826 1409.7773 1289.2466
1723 1724 1725 1726 1727 1728
1681.5907 1526.6534 1511.4934 1839.2565 1828.8065 1513.1740
1729 1730 1731 1732 1733 1734
1828.0112 1854.3798 1543.6910 1853.9260 1226.4956 1581.1783
1735 1736 1737 1738 1739 1740
1345.3774 1093.7892 1552.6098 1400.2154 1247.9753 1771.0974
1741 1742 1743 1744 1745 1746
1484.4490 1466.7165 1481.0568 1475.0148 1462.6330 1441.0063
1747 1748 1749 1750 1751 1752
1421.0568 1781.2471 1458.4543 1444.3412 1470.9714 1399.0001
1753 1754 1755 1756 1757 1758
2039.3912 1484.1775 1326.4015 1691.9320 1363.5852 1391.5344
1759 1760 1761 1762 1763 1764
1388.7494 1659.1071 1407.5032 1373.9422 1375.1232 1365.8069
1765 1766 1767 1768 1769 1770
1376.7801 1532.9099 1526.6534 1534.5110 1558.1114 2250.0795
1771 1772 1773 1774 1775 1776
1551.7520 1462.3756 1501.8596 1504.0096 1514.9553 1519.7413
1777 1778 1779 1780 1781 1782
1506.1650 1514.9582 1520.3120 1826.7789 1850.1387 1746.1828
1783 1784 1785 1786 1787 1788
1695.8990 1403.1439 1432.6038 1773.4331 1754.5311 1770.2598
1789 1790 1791 1792 1793 1794
1764.2401 1411.6159 1507.8315 1177.2351 1417.7659 1404.3006
1795 1796 1797 1798 1799 1800
1395.7930 1508.8457 1531.8654 1521.9240 1853.5627 1548.9612
1801 1802 1803 1804 1805 1806
1843.4861 1646.1999 1648.1226 1633.2011 1358.1006 1371.2377
1807 1808 1809 1810 1811 1812
1538.8154 9583.6834 1549.2475 2237.9728 1494.4505 1432.1415
1813 1814 1815 1816 1817 1818
1479.1545 1487.5202 1442.3003 1797.1373 1402.0087 1476.8527
1819 1820 1821 1822 1823 1824
1865.3307 1532.3398 2231.6116 1468.7360 2684.0654 1545.9002
1825 1826 1827 1828 1829 1830
1544.7459 1511.5915 1561.7761 1847.0418 1804.3844 1532.2344
1831 1832 1833 1834 1835 1836
1513.8462 1523.1618 1836.7678 1502.4397 1519.1989 1530.4469
1837 1838 1839 1840 1841 1842
1509.3438 1503.2597 1523.2361 1181.7111 1482.1556 1796.8866
1843 1844 1845 1846 1847 1848
1158.5555 1507.7469 1822.2509 1755.4827 1828.9197 1836.6174
1849 1850 1851 1852 1853 1854
1840.5276 1523.1618 1492.5906 1505.2260 1516.5738 1513.1850
1855 1856 1857 1858 1859 1860
1217.0455 1528.0736 1813.5768 1537.0142 1521.2463 1525.1401
1861 1862 1863 1864 1865 1866
1543.3132 1508.5750 1541.0093 1378.5810 1818.1790 1667.3165
1867 1868 1869 1870 1871 1872
1515.2464 1512.1429 1498.3408 1474.4050 1574.4266 1564.9760
1873 1874 1875 1876 1877 1878
1558.3994 1531.9595 1552.4742 1580.4920 1518.2519 1876.3535
1879 1880 1881 1882 1883 1884
1530.4469 1766.2112 1784.9125 1791.7922 1469.7482 1801.9096
1885 1886 1887 1888 1889 1890
1472.8394 1448.1427 1490.8407 1541.0206 1492.3956 1494.6429
1891 1892 1893 1894 1895 1896
1816.4955 1502.6874 1817.2759 1431.2201 1512.5241 1510.4635
1897 1898 1899 1900 1901 1902
1505.0860 1805.4538 1495.0197 1567.3157 1566.2478 1565.8644
1903 1904 1905 1906 1907 1908
1578.7252 1572.3553 1574.4266 1139.0302 1509.3298 1833.2346
1909 1910 1911 1912 1913 1914
1446.2990 1555.6079 1571.2984 1585.8878 1538.8154 1810.7380
1915 1916 1917 1918 1919 1920
1498.7046 1490.8975 1493.0615 1792.2178 1479.5029 1396.6235
1921 1922 1923 1924 1925 1926
1434.4463 1557.3261 1551.0743 1554.7253 1585.8878 1826.3319
1927 1928 1929 1930 1931 1932
2201.6751 1515.8095 1820.2201 1493.1424 1499.8326 1493.7002
1933 1934 1935 1936 1937 1938
1571.2103 1343.3018 1765.5456 1901.7562 1257.5140 1836.2046
1939 1940 1941 1942 1943 1944
1880.3237 1806.4266 1572.8400 1552.6146 1169.9687 1555.3145
1945 1946 1947 1948 1949 1950
1522.7891 1543.2124 1526.8440 1847.4975 1823.7211 2139.2663
1951 1952 1953 1954 1955 1956
1820.6790 1838.5966 1518.7227 1206.4296 1777.3986 1696.2509
1957 1958 1959 1960 1961 1962
1416.7637 3016.1613 1022.8980 1718.2557 1413.9488 1469.7189
1963 1964 1965 1966 1967 1968
1405.3761 1261.2495 1433.7391 1527.4948 1858.9877 1758.6362
1969 1970 1971 1972 1973 1974
1424.3274 1852.1958 1450.3551 1332.1314 1546.8337 1856.2090
1975 1976 1977 1978 1979 1980
1855.8453 1863.9206 1855.7412 1538.9821 1550.6947 1866.6026
1981 1982 1983 1984 1985 1986
1847.2526 1375.4744 1353.6393 1540.1321 1759.1877 1529.2930
1987 1988 1989 1990 1991 1992
1527.2923 1535.7580 1256.1353 1514.1609 1464.2777 1726.0541
1993 1994 1995 1996 1997 1998
1800.8353 1845.5726 1821.4612 1813.9315 1519.9702 1528.6555
1999 2000 2001 2002 2003 2004
1841.7826 1521.0645 1521.4591 1854.3798 1854.3798 2206.5660
2005 2006 2007 2008 2009 2010
1851.4529 1255.1975 1216.7865 1489.6279 2091.1179 2604.0945
2011 2012 2013 2014 2015 2016
1804.9405 1788.8810 1596.3440 1580.1052 1531.9595 1463.9492
2017 2018 2019 2020 2021 2022
1597.4325 1585.4997 1450.4421 1540.9146 1577.2633 1525.1594
2023 2024 2025 2026 2027 2028
1514.6037 1560.3329 1573.3538 1543.6097 1549.0696 1428.3246
2029 2030 2031 2032 2033 2034
1525.3938 1840.3869 1533.6628 1544.8302 1532.1597 1850.7786
2035 2036 2037 2038 2039 2040
1134.6643 1845.3081 1845.3081 1473.3398 1502.2492 1699.4818
2041 2042 2043 2044 2045 2046
1979.0128 1426.4493 1481.3550 1489.0746 1503.4561 1507.3779
2047 2048 2049 2050 2051 2052
1502.2492 1811.3260 1837.2174 1823.4934 1750.1761 1474.0205
2053 2054 2055 2056 2057 2058
1830.5050 1816.0509 1575.5001 2484.5946 2440.1840 1280.9614
2059 2060 2061 2062 2063 2064
1198.3124 1116.4817 1512.1539 1429.8619 1549.6380 1814.5498
2065 2066 2067 2068 2069 2070
2621.7636 1454.1660 1840.9782 1836.6174 1827.5505 1836.0685
2071 2072 2073 2074 2075 2076
2196.8626 1831.8390 1515.3203 1509.2342 1322.0509 1323.2736
2077 2078 2079 2080 2081 2082
1522.4965 1521.4451 1526.1800 1215.9090 1516.1555 1876.9305
2083 2084 2085 2086 2087 2088
1300.8386 1517.6878 1532.1597 2263.1132 1908.5018 1898.5875
2089 2090 2091 2092 2093 2094
1426.8117 1575.7798 1514.9582 1775.4090 1574.2519 1571.5774
2095 2096 2097 2098 2099 2100
1504.0096 1481.8036 1523.9076 1502.5078 1496.1966 1785.1169
2101 2102 2103 2104 2105 2106
1525.7428 1540.9146 1560.2370 1820.6594 1420.6395 1868.6952
2107 2108 2109 2110 2111 2112
1129.0842 1548.5821 1347.0902 1298.8298 1038.4911 1523.9211
2113 2114 2115 2116 2117 2118
2694.2597 2298.5557 1566.4908 1996.2584 1992.7905 1579.3138
2119 2120 2121 2122 2123 2124
1524.9438 1507.7631 1823.8438 1518.6428 1522.7891 1545.0404
2125 2126 2127 2128 2129 2130
1530.0723 1832.5670 1474.4934 1516.5738 1508.3263 1387.8096
2131 2132 2133 2134 2135 2136
1122.1022 1552.8972 1553.9561 1539.4878 1555.9887 1555.9887
2137 2138 2139 2140 2141 2142
1122.1022 1964.9423 1343.5277 1120.7647 1795.3789 1335.6543
2143 2144 2145 2146 2147 2148
1463.7124 1142.6745 1456.7344 1764.5147 1470.6278 1488.4127
2149 2150 2151 2152 2153 2154
1963.3468 1446.9676 1979.7049 1492.1032 1220.6709 1547.9046
2155 2156 2157 2158 2159 2160
1729.0561 1393.6765 1841.1812 1843.7963 1496.1966 1556.4132
2161 2162 2163 2164 2165 2166
1532.9099 1165.1957 1823.1396 2224.0197 2175.8758 2214.5084
2167 2168 2169 2170 2171 2172
1265.2474 1276.2333 1257.0748 1089.3350 1401.3794 1543.6761
2173 2174 2175 2176 2177 2178
1233.1546 1413.5065 1712.8282 1402.9763 1579.7864 1435.6533
2179 2180 2181 2182 2183 2184
1312.9814 1470.6335 1482.4324 1786.6513 1488.4242 1504.0096
2185 2186 2187 2188 2189 2190
1767.8197 1703.1796 1699.4782 1338.6993 1467.6292 1473.7265
2191 2192 2193 2194 2195 2196
1465.1647 1452.4046 1775.2801 1619.8094 1294.0508 1280.2863
2197 2198 2199 2200 2201 2202
1610.1013 1897.8780 1506.0585 1616.0353 1601.6738 1471.0433
2203 2204 2205 2206 2207 2208
1452.7489 1591.8548 1594.7260 1748.4319 1488.4242 1478.3130
2209 2210 2211 2212 2213 2214
1479.0312 1766.1861 1756.6797 1476.6604 1459.0104 1760.1769
2215 2216 2217 2218 2219 2220
1760.8181 1472.5299 1772.9903 1776.1718 1459.6479 1537.5659
2221 2222 2223 2224 2225 2226
1339.2036 1522.8692 1498.8924 1523.9076 1521.6017 1498.3298
2227 2228 2229 2230 2231 2232
1328.6993 1534.6254 1469.2610 1467.6157 1769.3228 1467.6980
2233 2234 2235 2236 2237 2238
1775.2801 1752.8333 1306.9048 1362.6571 1324.5192 1868.6607
2239 2240 2241 2242 2243 2244
1308.1160 1725.4669 1100.8896 1209.2040 1241.0944 1391.1552
2245 2246 2247 2248 2249 2250
1393.1467 1208.3675 1283.1999 2210.6700 1548.1805 1847.6068
2251 2252 2253 2254 2255 2256
1854.3762 1537.0030 1536.5182 1540.7968 1502.2383 1461.6953
2257 2258 2259 2260 2261 2262
1458.0722 NA 1469.0741 1484.7399 1483.7282 1487.0293
2263 2264 2265 2266 2267 2268
1492.0224 1478.3073 1484.7399 1464.7105 1470.2736 1482.6340
2269 2270 2271 2272 2273 2274
1542.7733 1008.5180 1440.7747 1459.0787 1461.0007 1462.9939
2275 2276 2277 2278 2279 2280
1459.0104 1457.6353 1758.6362 1292.7969 1439.3910 1452.2530
2281 2282 2283 2284 2285 2286
1858.9877 1548.9612 1872.8665 1874.2487 1866.2504 1555.9714
2287 2288 2289 2290 2291 2292
1337.1203 1291.1661 1317.2878 1093.1674 1149.2046 1514.1778
2293 2294 2295 2296 2297 2298
1564.9786 1199.4968 2369.7529 1520.7946 3850.7467 1509.4992
2299 2300 2301 2302 2303 2304
1180.2312 1298.5659 1311.3383 1322.9592 1589.9177 1532.2344
2305 2306 2307 2308 2309 2310
1106.2375 1268.0210 1125.3578 1289.2768 1241.9406 1460.8059
2311 2312 2313 2314 2315 2316
1412.4735 1203.9762 1323.6167 1488.5753 1686.1513 1571.0076
2317 2318 2319 2320 2321 2322
1515.2493 1526.6534 1538.5109 1847.7914 1532.1031 1513.1850
2323 2324 2325 2326 2327 2328
1516.6537 1597.3677 1839.7237 1543.0138 1543.6910 1429.6081
2329 2330 2331 2332 2333 2334
1481.4680 1265.9208 1533.5797 1830.9665 1384.2672 1370.9897
2335 2336 2337 2338 2339 2340
1821.9009 1826.4280 1828.9197 1826.4280 1828.9197 1513.9259
2341 2342 2343 2344 2345 2346
1494.1583 1611.7322 1524.5735 1838.4701 1294.9597 1376.8318
2347 2348 2349 2350 2351 2352
1523.6915 1540.9146 1506.0614 1497.2168 1518.7227 1499.0228
2353 2354 2355 2356 2357 2358
1531.4905 1526.7249 1522.8692 1512.8943 1491.9278 1196.4792
2359 2360
1521.0645 1282.6056
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